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Tran, Khanh-Hung. "Semi-supervised dictionary learning and Semi-supervised deep neural network." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPASP014.
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Depuis les années 2010, l’apprentissage automatique (ML) est l’un des sujets qui retient beaucoup l'attention des chercheurs scientifiques. De nombreux modèles de ML ont démontré leur capacité produire d’excellent résultats dans des divers domaines comme Vision par ordinateur, Traitement automatique des langues, Robotique… Toutefois, la plupart de ces modèles emploient l’apprentissage supervisé, qui requiert d’un massive annotation. Par conséquent, l’objectif de cette thèse est d’étudier et de proposer des approches semi-supervisées qui ont plusieurs avantages par rapport à l’apprentissage supervisé. Au lieu d’appliquer directement un classificateur semi-supervisé sur la représentation originale des données, nous utilisons plutôt des types de modèle qui intègrent une phase de l’apprentissage de représentation avant de la phase de classification, pour mieux s'adapter à la non linéarité des données. Dans le premier temps, nous revisitons des outils qui permettent de construire notre modèles semi-supervisés. Tout d’abord, nous présentons deux types de modèle qui possèdent l’apprentissage de représentation dans leur architecture : l’apprentissage de dictionnaire et le réseau de neurones, ainsi que les méthodes d’optimisation pour chaque type de model, en plus, dans le cas de réseau de neurones, nous précisons le problème avec les exemples contradictoires. Ensuite, nous présentons les techniques qui accompagnent souvent avec l’apprentissage semi-supervisé comme l’apprentissage de variétés et le pseudo-étiquetage. Dans le deuxième temps, nous travaillons sur l’apprentissage de dictionnaire. Nous synthétisons en général trois étapes pour construire un modèle semi-supervisée à partir d’un modèle supervisé. Ensuite, nous proposons notre modèle semi-supervisée pour traiter le problème de classification typiquement dans le cas d’un faible nombre d’échantillons d’entrainement (y compris tous labellisés et non labellisés échantillons). D'une part, nous appliquons la préservation de la structure de données de l’espace original à l’espace de code parcimonieux (l’apprentissage de variétés), ce qui est considéré comme la régularisation pour les codes parcimonieux. D'autre part, nous intégrons un classificateur semi-supervisé dans l’espace de code parcimonieux. En outre, nous effectuons le codage parcimonieux pour les échantillons de test en prenant en compte aussi la préservation de la structure de données. Cette méthode apporte une amélioration sur le taux de précision par rapport à des méthodes existantes. Dans le troisième temps, nous travaillons sur le réseau de neurones. Nous proposons une approche qui s’appelle "manifold attack" qui permets de renforcer l’apprentissage de variétés. Cette approche est inspirée par l’apprentissage antagoniste : trouver des points virtuels qui perturbent la fonction de coût sur l’apprentissage de variétés (en la maximisant) en fixant les paramètres du modèle; ensuite, les paramètres du modèle sont mis à jour, en minimisant cette fonction de coût et en fixant les points virtuels. Nous fournissons aussi des critères pour limiter l’espace auquel les points virtuels appartiennent et la méthode pour les initialiser. Cette approche apporte non seulement une amélioration sur le taux de précision mais aussi une grande robustesse contre les exemples contradictoires. Enfin, nous analysons des similarités et des différences, ainsi que des avantages et inconvénients entre l’apprentissage de dictionnaire et le réseau de neurones. Nous proposons quelques perspectives sur ces deux types de modèle. Dans le cas de l’apprentissage de dictionnaire semi-supervisé, nous proposons quelques techniques en inspirant par le réseau de neurones. Quant au réseau de neurones, nous proposons d’intégrer "manifold attack" sur les modèles génératifsSince the 2010's, machine learning (ML) has been one of the topics that attract a lot of attention from scientific researchers. Many ML models have been demonstrated their ability to produce excellent results in various fields such as Computer Vision, Natural Language Processing, Robotics... However, most of these models use supervised learning, which requires a massive annotation. Therefore, the objective of this thesis is to study and to propose semi-supervised learning approaches that have many advantages over supervised learning. Instead of directly applying a semi-supervised classifier on the original representation of data, we rather use models that integrate a representation learning stage before the classification stage, to better adapt to the non-linearity of the data. In the first step, we revisit tools that allow us to build our semi-supervised models. First, we present two types of model that possess representation learning in their architecture: dictionary learning and neural network, as well as the optimization methods for each type of model. Moreover, in the case of neural network, we specify the problem with adversarial examples. Then, we present the techniques that often accompany with semi-supervised learning such as variety learning and pseudo-labeling. In the second part, we work on dictionary learning. We synthesize generally three steps to build a semi-supervised model from a supervised model. Then, we propose our semi-supervised model to deal with the classification problem typically in the case of a low number of training samples (including both labelled and non-labelled samples). On the one hand, we apply the preservation of the data structure from the original space to the sparse code space (manifold learning), which is considered as regularization for sparse codes. On the other hand, we integrate a semi-supervised classifier in the sparse code space. In addition, we perform sparse coding for test samples by taking into account also the preservation of the data structure. This method provides an improvement on the accuracy rate compared to other existing methods. In the third step, we work on neural network models. We propose an approach called "manifold attack" which allows reinforcing manifold learning. This approach is inspired from adversarial learning : finding virtual points that disrupt the cost function on manifold learning (by maximizing it) while fixing the model parameters; then the model parameters are updated by minimizing this cost function while fixing these virtual points. We also provide criteria for limiting the space to which the virtual points belong and the method for initializing them. This approach provides not only an improvement on the accuracy rate but also a significant robustness to adversarial examples. Finally, we analyze the similarities and differences, as well as the advantages and disadvantages between dictionary learning and neural network models. We propose some perspectives on both two types of models. In the case of semi-supervised dictionary learning, we propose some techniques inspired by the neural network models. As for the neural network, we propose to integrate manifold attack on generative models
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Morns, Ian Philip. "The novel dynamic supervised forward propagation neural network for handwritten character recognition." Thesis, University of Newcastle Upon Tyne, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285741.
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Syrén, Grönfelt Natalie. "Pretraining a Neural Network for Hyperspectral Images Using Self-Supervised Contrastive Learning." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-179122.
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Hyperspectral imaging is an expanding topic within the field of computer vision, that uses images of high spectral granularity. Contrastive learning is a discrim- inative approach to self-supervised learning, a form of unsupervised learning where the network is trained using self-created pseudo-labels. This work com- bines these two research areas and investigates how a pretrained network based on contrastive learning can be used for hyperspectral images. The hyperspectral images used in this work are generated from simulated RGB images and spec- tra from a spectral library. The network is trained with a pretext task based on data augmentations, and is evaluated through transfer learning and fine-tuning for a downstream task. The goal is to determine the impact of the pretext task on the downstream task and to determine the required amount of labelled data. The results show that the downstream task (a classifier) based on the pretrained network barely performs better than a classifier without a pretrained network. In the end, more research needs to be done to confirm or reject the benefit of a pretrained network based on contrastive learning for hyperspectral images. Also, the pretrained network should be tested on real-world hyperspectral data and trained with a pretext task designed for hyperspectral images.
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Bylund, Andreas, Anton Erikssen, and Drazen Mazalica. "Hyperparameters impact in a convolutional neural network." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18670.
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Machine learning and image recognition is a big and growing subject in today's society. Therefore the aim of this thesis is to compare convolutional neural networks with different hyperparameter settings and see how the hyperparameters affect the networks test accuracy in identifying images of traffic signs. The reason why traffic signs are chosen as objects to evaluate hyperparameters is due to the author's previous experience in the domain. The object itself that is used for image recognition does not matter. Any dataset with images can be used to see the hyperparameters affect. Grid search is used to create a large amount of models with different width and depth, learning rate and momentum. Convolution layers, activation functions and batch size are all tested separately. These experiments make it possible to evaluate how the hyperparameters affect the networks in their performance of recognizing images of traffic signs. The models are created using Keras API and then trained and tested on the dataset Traffic Signs Preprocessed. The results show that hyperparameters affect test accuracy, some affect more than others. Configuring learning rate and momentum can in some cases result in disastrous results if they are set too high or too low. Activation function also show to be a crucial hyperparameter where it in some cases produce terrible results.
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Schembri, Massimo. "Anomaly Prediction in Production Supercomputer with Convolution and Semi-supervised autoencoder." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22379/.
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Un sistema HPC (High Performance Computing) è un sistema con capacità computazionali molto elevate adatto a task molto esigenti in termini di risorse. Alcune delle proprietà fondamentali di un sistema del genere sono certamente la disponibilità e l'affidabilità che possono essere messe a rischio da problemi hardware e software. In quest'attività di tesi si è realizzato e analizzato le performance di un sistema di anomaly detection in termini di capacità di rilevazione e predizione di un'anomalia su vari nodi di un sistema HPC, in particolare utilizzando i dati relativi al sistema MARCONI del consorzio CINECA.
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Guo, Lilin. "A Biologically Plausible Supervised Learning Method for Spiking Neurons with Real-world Applications." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2982.
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Learning is central to infusing intelligence to any biologically inspired system. This study introduces a novel Cross-Correlated Delay Shift (CCDS) learning method for spiking neurons with the ability to learn and reproduce arbitrary spike patterns in a supervised fashion with applicability tospatiotemporalinformation encoded at the precise timing of spikes. By integrating the cross-correlated term,axonaland synapse delays, the CCDS rule is proven to be both biologically plausible and computationally efficient. The proposed learning algorithm is evaluated in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. The results indicate that the proposed CCDS learning rule greatly improves classification accuracy when compared to the standards reached with the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule.
Network structureis the crucial partforany application domain of Artificial Spiking Neural Network (ASNN). Thus, temporal learning rules in multilayer spiking neural networks are investigated. As extensions of single-layer learning rules, the multilayer CCDS (MutCCDS) is also developed. Correlated neurons are connected through fine-tuned weights and delays. In contrast to the multilayer Remote Supervised Method (MutReSuMe) and multilayertempotronrule (MutTmptr), the newly developed MutCCDS shows better generalization ability and faster convergence. The proposed multilayer rules provide an efficient and biologically plausible mechanism, describing how delays and synapses in the multilayer networks are adjusted to facilitate learning.
Interictalspikes (IS) aremorphologicallydefined brief events observed in electroencephalography (EEG) records from patients with epilepsy. The detection of IS remains an essential task for 3D source localization as well as in developing algorithms for seizure prediction and guided therapy. In this work, we present a new IS detection method using the Wavelet Encoding Device (WED) method together with CCDS learning rule and a specially designed Spiking Neural Network (SNN) structure. The results confirm the ability of such SNN to achieve good performance for automatically detecting such events from multichannel EEG records.
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Hansen, Vedal Amund. "Comparing performance of convolutional neural network models on a novel car classification task." Thesis, KTH, Medieteknik och interaktionsdesign, MID, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213468.
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Recent neural network advances have lead to models that can be used for a variety of image classification tasks, useful for many of today’s media technology applications. In this paper, I train hallmark neural network architectures on a newly collected vehicle image dataset to do both coarse- and fine-grained classification of vehicle type. The results show that the neural networks can learn to distinguish both between many very different and between a few very similar classes, reaching accuracies of 50.8% accuracy on 28 classes and 61.5% in the most challenging 5, despite noisy images and labeling of the dataset.Nya neurala nätverksframsteg har lett till modeller som kan användas för en mängd olika bildklasseringsuppgifter, och är därför användbara många av dagens medietekniska applikationer. I detta projektet tränar jag moderna neurala nätverksarkitekturer på en nyuppsamlad bilbild-datasats för att göra både grov- och finkornad klassificering av fordonstyp. Resultaten visar att neurala nätverk kan lära sig att skilja mellan många mycket olika bilklasser, och även mellan några mycket liknande klasser. Mina bästa modeller nådde 50,8% träffsäkerhet vid 28 klasser och 61,5% på de mest utmanande 5, trots brusiga bilder och manuell klassificering av datasetet.
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Karlsson, Erik, and Gilbert Nordhammar. "Naive semi-supervised deep learning med sammansättning av pseudo-klassificerare." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17177.
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Ett vanligt problem inom supervised learning är brist på taggad träningsdata. Naive semi-supervised deep learning är en träningsteknik som ämnar att mildra detta problem genom att generera pseudo-taggad data och därefter låta ett neuralt nätverk träna på denna samt en mindre mängd taggad data. Detta arbete undersöker om denna teknik kan förbättras genom användandet av röstning. Flera neurala nätverk tränas genom den framtagna tekniken, naive semi-supervised deep learning eller supervised learning och deras träffsäkerhet utvärderas därefter. Resultaten visade nästan enbart försämringar då röstning användes. Dock verkar inte förutsättningarna för röstning ha varit särskilt goda, vilket gör det svårt att dra en säker slutsats kring effekterna av röstning. Även om röstning inte gav förbättringar har NSSDL visat sig vara mycket effektiv. Det finns flera applikationsområden där tekniken i framtiden skulle kunna användas med goda resultat.
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Flores, Quiroz Martín. "Descriptive analysis of the acquisition of the base form, third person singular, present participle regular past, irregular past, and past participle in a supervised artificial neural network and an unsupervised artificial neural network." Tesis, Universidad de Chile, 2013. http://www.repositorio.uchile.cl/handle/2250/115653.
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Tesis para optar al grado de Magíster en Lingüistica mención Lengua InglesaStudying children’s language acquisition in natural settings is not cost and time effective. Therefore, language acquisition may be studied in an artificial setting reducing the costs related to this type of research. By artificial, I do not mean that children will be placed in an artificial setting, first because this would not be ethical and second because the problem of the time needed for this research would still be present. Thus, by artificial I mean that the tools of simulation found in artificial intelligence can be used. Simulators as artificial neural networks (ANNs) possess the capacity to simulate different human cognitive skills, as pattern or speech recognition, and can also be implemented in personal computers with software such as MATLAB, a numerical computing software. ANNs are computer simulation models that try to resemble the neural processes behind several human cognitive skills. There are two main types of ANNs: supervised and unsupervised. The learning processes in the first are guided by the computer programmer, while the learning processes of the latter are random.
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Dabiri, Sina. "Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/86845.
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Identification of travelers' transportation modes is a fundamental step for various problems that arise in the domain of transportation such as travel demand analysis, transport planning, and traffic management. This thesis aims to identify travelers' transportation modes purely based on their GPS trajectories. First, a segmentation process is developed to partition a user's trip into GPS segments with only one transportation mode. A majority of studies have proposed mode inference models based on hand-crafted features, which might be vulnerable to traffic and environmental conditions. Furthermore, the classification task in almost all models have been performed in a supervised fashion while a large amount of unlabeled GPS trajectories has remained unused. Accordingly, a deep SEmi-Supervised Convolutional Autoencoder (SECA) architecture is proposed to not only automatically extract relevant features from GPS segments but also exploit useful information in unlabeled data. The SECA integrates a convolutional-deconvolutional autoencoder and a convolutional neural network into a unified framework to concurrently perform supervised and unsupervised learning. The two components are simultaneously trained using both labeled and unlabeled GPS segments, which have already been converted into an efficient representation for the convolutional operation. An optimum schedule for varying the balancing parameters between reconstruction and classification errors are also implemented. The performance of the proposed SECA model, trip segmentation, the method for converting a raw trajectory into a new representation, the hyperparameter schedule, and the model configuration are evaluated by comparing to several baselines and alternatives for various amounts of labeled and unlabeled data. The experimental results demonstrate the superiority of the proposed model over the state-of-the-art semi-supervised and supervised methods with respect to metrics such as accuracy and F-measure.Master of Science
Identifying users' transportation modes (e.g., bike, bus, train, and car) is a key step towards many transportation related problems including (but not limited to) transport planning, transit demand analysis, auto ownership, and transportation emissions analysis. Traditionally, the information for analyzing travelers' behavior for choosing transport mode(s) was obtained through travel surveys. High cost, low-response rate, time-consuming manual data collection, and misreporting are the main demerits of the survey-based approaches. With the rapid growth of ubiquitous GPS-enabled devices (e.g., smartphones), a constant stream of users' trajectory data can be recorded. A user's GPS trajectory is a sequence of GPS points, recorded by means of a GPS-enabled device, in which a GPS point contains the information of the device geographic location at a particular moment. In this research, users' GPS trajectories, rather than traditional resources, are harnessed to predict their transportation mode by means of statistical models. With respect to the statistical models, a wide range of studies have developed travel mode detection models using on hand-designed attributes and classical learning techniques. Nonetheless, hand-crafted features cause some main shortcomings including vulnerability to traffic uncertainties and biased engineering justification in generating effective features. A potential solution to address these issues is by leveraging deep learning frameworks that are capable of capturing abstract features from the raw input in an automated fashion. Thus, in this thesis, deep learning architectures are exploited in order to identify transport modes based on only raw GPS tracks. It is worth noting that a significant portion of trajectories in GPS data might not be annotated by a transport mode and the acquisition of labeled data is a more expensive and labor-intensive task in comparison with collecting unlabeled data. Thus, utilizing the unlabeled GPS trajectory (i.e., the GPS trajectories that have not been annotated by a transport mode) is a cost-effective approach for improving the prediction quality of the travel mode detection model. Therefore, the unlabeled GPS data are also leveraged by developing a novel deep-learning architecture that is capable of extracting information from both labeled and unlabeled data. The experimental results demonstrate the superiority of the proposed models over the state-of-the-art methods in literature with respect to several performance metrics.
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Umbach, Simon Lineu [Verfasser], Jörg [Gutachter] Breitung, and Robinson [Gutachter] Kruse-Becher. "Macroeconomic Forecasting and Evaluation with Supervised and Neural Network Reinforced Factor Models / Simon Lineu Umbach ; Gutachter: Jörg Breitung, Robinson Kruse-Becher." Köln : Universitäts- und Stadtbibliothek Köln, 2021. http://d-nb.info/1236341244/34.
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Apprey-Hermann, Joseph Kwame. "Evaluating The Predictability of Pseudo-Random Number Generators Using Supervised Machine Learning Algorithms." Youngstown State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1588805461290138.
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Truzzi, Stefano. "Event classification in MAGIC through Convolutional Neural Networks." Doctoral thesis, Università di Siena, 2022. http://hdl.handle.net/11365/1216295.
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The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescopes are able to detect gamma rays from the ground with energies beyond several tens of GeV emitted by the most energetic known objects, including Pulsar Wind Nebulae, Active Galactic Nuclei, and Gamma-Ray Bursts.
Gamma rays and cosmic rays are detected by imaging the Cherenkov light produced by the charged superluminal leptons in the extended air shower originated when the primary particle interacts with the atmosphere. These Cherenkov flashes brighten the night sky for short times in the nanosecond scale. From the image topology and other observables, gamma rays can be separated from the unwanted cosmic rays, and thereafter incoming direction and energy of the primary gamma rays can be reconstructed.
The standard algorithm in MAGIC data analysis for the gamma/hadron separation is the so-called Random Forest, that works on a parametrization of the stereo events based on the shower image parameters. Until a few years ago, these algorithms were limited by the computational resources but modern devices, such as GPUs, make it possible to work efficiently on the pixel maps information. Most neural network applications in the field perform the training on Monte Carlo simulated data for the gamma-ray sample. This choice is prone to systematics arising from discrepancies between observational data and simulations.
Instead, in this thesis I trained a known neural network scheme with observation data from a giant flare of the bright TeV blazar Mrk421 observed by MAGIC in 2013. With this method for gamma/hadron separation, the preliminary results compete with the standard MAGIC analysis based on Random Forest classification, which also shows the potential of this approach for further improvement.
In this thesis first an introduction to the High-Energy Astrophysics and the Astroparticle physics is given. The cosmic messengers are briefly reviewed, with a focus on the photons, then astronomical sources of γ rays are described, followed by a description of the detection techniques.
In the second chapter the MAGIC analysis pipeline starting from the low level data acquisition to the high level data is described. The MAGIC Instrument Response Functions are detailed. Finally, the most important astronomical sources used in the standard MAGIC analysis are listed.
The third chapter is devoted to Deep Neural Network techniques, starting from an historical Artificial Intelligence excursus followed by a Machine Learning description. The basic principles behind an Artificial Neural Network and the Convolutional Neural Network used for this work are explained.
Last chapter describes my original work, showing in detail the data selection/manipulation for training the Inception Resnet V2 Convolutional Neural Network and the preliminary results obtained from four test sources.
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Granström, Daria, and Johan Abrahamsson. "Loan Default Prediction using Supervised Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252312.
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It is essential for a bank to estimate the credit risk it carries and the magnitude of exposure it has in case of non-performing customers. Estimation of this kind of risk has been done by statistical methods through decades and with respect to recent development in the field of machine learning, there has been an interest in investigating if machine learning techniques can perform better quantification of the risk. The aim of this thesis is to examine which method from a chosen set of machine learning techniques exhibits the best performance in default prediction with regards to chosen model evaluation parameters. The investigated techniques were Logistic Regression, Random Forest, Decision Tree, AdaBoost, XGBoost, Artificial Neural Network and Support Vector Machine. An oversampling technique called SMOTE was implemented in order to treat the imbalance between classes for the response variable. The results showed that XGBoost without implementation of SMOTE obtained the best result with respect to the chosen model evaluation metric.Det är nödvändigt för en bank att ha en bra uppskattning på hur stor risk den bär med avseende på kunders fallissemang. Olika statistiska metoder har använts för att estimera denna risk, men med den nuvarande utvecklingen inom maskininlärningsområdet har det väckt ett intesse att utforska om maskininlärningsmetoder kan förbättra kvaliteten på riskuppskattningen. Syftet med denna avhandling är att undersöka vilken metod av de implementerade maskininlärningsmetoderna presterar bäst för modellering av fallissemangprediktion med avseende på valda modelvaldieringsparametrar. De implementerade metoderna var Logistisk Regression, Random Forest, Decision Tree, AdaBoost, XGBoost, Artificiella neurala nätverk och Stödvektormaskin. En översamplingsteknik, SMOTE, användes för att behandla obalansen i klassfördelningen för svarsvariabeln. Resultatet blev följande: XGBoost utan implementering av SMOTE visade bäst resultat med avseende på den valda metriken.
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Vignisson, Egill. "Anomaly Detection in Streaming Data from a Sensor Network." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-257507.
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In this thesis, the use of unsupervised and semi-supervised machine learning techniques was analyzed as potential tools for anomaly detection in the sensor network that the electrical system in a Scania truck is comprised of. The experimentation was designed to analyse the need for both point and contextual anomaly detection in this setting. For the point anomaly detection the method of Isolation Forest was experimented with and for contextual anomaly detection two different recurrent neural network architectures using Long Short Term Memory units was relied on. One model was simply a many to one regression model trained to predict a certain signal, while the other was an encoder-decoder network trained to reconstruct a sequence. Both models were trained in an semi-supervised manner, i.e. on data that only depicts normal behaviour, which theoretically should lead to a performance drop on abnormal sequences resulting in higher error terms. In both setting the parameters of a Gaussian distribution were estimated using these error terms which allowed for a convenient way of defining a threshold which would decide if the observation would be flagged as anomalous or not. Additional experimentation's using an exponential weighted moving average over a number of past observations to filter the signal was also conducted. The models performance on this particular task was very different but the regression model showed a lot of promise especially when combined with a filtering preprocessing step to reduce the noise in the data. However the model selection will always be governed by the nature the particular task at hand so the other methods might perform better in other settings.I den här avhandlingen var användningen av oövervakad och halv-övervakad maskininlärning analyserad som ett möjligt verktyg för att upptäcka avvikelser av anomali i det sensornätverk som elektriska systemet en Scanialastbil består av. Experimentet var konstruerat för att analysera behovet av både punkt och kontextuella avvikelser av anomali i denna miljö. För punktavvikelse av anomali var metoden Isolation Forest experimenterad med och för kontextuella avvikelser av anomali användes två arkitekturer av återkommande neurala nätverk. En av modellerna var helt enkelt många-till-en regressionmodell tränad för att förutspå ett visst märke, medan den andre var ett kodare-avkodare nätverk tränat för att rekonstruera en sekvens.Båda modellerna blev tränade på ett halv-övervakat sätt, d.v.s. på data som endast visar normalt beteende, som teoretiskt skulle leda till minskad prestanda på onormala sekvenser som ger ökat antal feltermer. I båda fallen blev parametrarna av en Gaussisk distribution estimerade på grund av dessa feltermer som tillåter ett bekvämt sätt att definera en tröskel som skulle bestämma om iakttagelsen skulle bli flaggad som en anomali eller inte. Ytterligare experiment var genomförda med exponentiellt viktad glidande medelvärde över ett visst antal av tidigare iakttagelser för att filtera märket. Modellernas prestanda på denna uppgift var välidt olika men regressionmodellen lovade mycket, särskilt kombinerad med ett filterat förbehandlingssteg för att minska bruset it datan. Ändå kommer modelldelen alltid styras av uppgiftens natur så att andra metoder skulle kunna ge bättre prestanda i andra miljöer.
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Knutsson, Magnus, and Linus Lindahl. "A COMPARATIVE STUDY OF FFN AND CNN WITHIN IMAGE RECOGNITION : The effects of training and accuracy of different artificial neural network designs." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17214.
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Image recognition and -classification is becoming more important as the need to be able to process large amounts of images is becoming more common. The aim of this thesis is to compare two types of artificial neural networks, FeedForward Network and Convolutional Neural Network, to see how these compare when performing the task of image recognition. Six models of each type of neural network was created that differed in terms of width, depth and which activation function they used in order to learn. This enabled the experiment to also see if these parameters had any effect on the rate which a network learn and how the network design affected the validation accuracy of the models. The models were implemented using the API Keras, and trained and tested using the dataset CIFAR-10. The results showed that within the scope of this experiment the CNN models were always preferable as they achieved a statistically higher validation accuracy compared to their FFN counterparts.
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Frasca, M. "GRAPH-BASED APPROACHES FOR IMBALANCED DATA IN FUNCTIONAL GENOMICS." Doctoral thesis, Università degli Studi di Milano, 2012. http://hdl.handle.net/2434/172445.
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The Gene Function Prediction (GFP) problem consists in inferring biological properties for the genes whose function is unknown or only partially
known, and raises challenging issues from both a machine learning and a
computational biology standpoint.
The GFP problem can be formalized as a semi-supervised learning problem in an undirected graph. Indeed, given a graph with a partial graph labeling, where nodes represent genes, edges functional relationships between
genes, and labels their membership to functional classes, GFP consists in
inferring the unknown functional classes of genes, by exploiting the topological relationships of the networks and the available a priori knowledge about
the functional properties of genes.
Several network-based machine learning algorithms have been proposed
for solving this problem, including Hopfield networks and label propagation
methods; however, some issues have been only partially considered, e.g. the
preservation of the prior knowledge and the unbalance between positive and
negative labels.
A first contribution of the thesis is the design of a Hopfield-based cost
sensitive neural network algorithm (COSNet) to address these learning issues. The method factorizes the solution of the problem in two parts: 1) the
subnetwork composed by the labelled vertices is considered, and the network
parameters are estimated through a supervised algorithm; 2) the estimated
parameters are extended to the subnetwork composed of the unlabeled vertices, and the attractor reached by the dynamics of this subnetwork allows
to predict the labeling of the unlabeled vertices.
The proposed method embeds in the neural algorithm the “a priori”
knowledge coded in the labeled part of the graph, and separates node labels
and neuron states, allowing to differentially weight positive and negative
node labels, and to perform a learning approach that takes into account the
“unbalance problem” that affects GFP.
A second contribution of this thesis is the development of a new algorithm (LSI ) which exploits some ideas of COSNet for evaluating the predictive capability of each input network. By this algorithm we can estimate the
effectiveness of each source of data for predicting a specific class, and then
we can use this information to appropriately integrate multiple networks by
weighting them according to an appropriate integration scheme.
Both COSNet and LSI are computationally efficient and scale well with
the dimension of the data.
COSNet and LSI have been applied to the genome-wide prediction of gene functions in the yeast and mouse model organisms, achieving results
comparable with those obtained with state-of-the-art semi-supervised and
supervised machine learning methods.
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Lundström, Love, and Oscar Öhman. "Machine Learning in credit risk : Evaluation of supervised machine learning models predicting credit risk in the financial sector." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-164101.
Full textAbstract:
When banks lend money to another party they face a risk that the borrower will not fulfill its obligation towards the bank. This risk is called credit risk and it’s the largest risk banks faces. According to the Basel accord banks need to have a certain amount of capital requirements to protect themselves towards future financial crisis. This amount is calculated for each loan with an attached risk-weighted asset, RWA. The main parameters in RWA is probability of default and loss given default. Banks are today allowed to use their own internal models to calculate these parameters. Thus hold capital with no gained interest is a great cost, banks seek to find tools to better predict probability of default to lower the capital requirement. Machine learning and supervised algorithms such as Logistic regression, Neural network, Decision tree and Random Forest can be used to decide credit risk. By training algorithms on historical data with known results the parameter probability of default (PD) can be determined with a higher certainty degree compared to traditional models, leading to a lower capital requirement. On the given data set in this article Logistic regression seems to be the algorithm with highest accuracy of classifying customer into right category. However, it classifies a lot of people as false positive meaning the model thinks a customer will honour its obligation but in fact the customer defaults. Doing this comes with a great cost for the banks. Through implementing a cost function to minimize this error, we found that the Neural network has the lowest false positive rate and will therefore be the model that is best suited for this specific classification task.När banker lånar ut pengar till en annan part uppstår en risk i att låntagaren inte uppfyller sitt antagande mot banken. Denna risk kallas för kredit risk och är den största risken en bank står inför. Enligt Basel föreskrifterna måste en bank avsätta en viss summa kapital för varje lån de ger ut för att på så sätt skydda sig emot framtida finansiella kriser. Denna summa beräknas fram utifrån varje enskilt lån med tillhörande risk-vikt, RWA. De huvudsakliga parametrarna i RWA är sannolikheten att en kund ej kan betala tillbaka lånet samt summan som banken då förlorar. Idag kan banker använda sig av interna modeller för att estimera dessa parametrar. Då bundet kapital medför stora kostnader för banker, försöker de sträva efter att hitta bättre verktyg för att uppskatta sannolikheten att en kund fallerar för att på så sätt minska deras kapitalkrav. Därför har nu banker börjat titta på möjligheten att använda sig av maskininlärningsalgoritmer för att estimera dessa parametrar. Maskininlärningsalgoritmer såsom Logistisk regression, Neurala nätverk, Beslutsträd och Random forest, kan användas för att bestämma kreditrisk. Genom att träna algoritmer på historisk data med kända resultat kan parametern, chansen att en kund ej betalar tillbaka lånet (PD), bestämmas med en högre säkerhet än traditionella metoder. På den givna datan som denna uppsats bygger på visar det sig att Logistisk regression är den algoritm med högst träffsäkerhet att klassificera en kund till rätt kategori. Däremot klassifiserar denna algoritm många kunder som falsk positiv vilket betyder att den predikterar att många kunder kommer betala tillbaka sina lån men i själva verket inte betalar tillbaka lånet. Att göra detta medför en stor kostnad för bankerna. Genom att istället utvärdera modellerna med hjälp av att införa en kostnadsfunktion för att minska detta fel finner vi att Neurala nätverk har den lägsta falsk positiv ration och kommer därmed vara den model som är bäst lämpad att utföra just denna specifika klassifierings uppgift.
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Rostami, Jako, and Fredrik Hansson. "Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385823.
Full textAbstract:
In this thesis, we examine the performance of different forecasting methods. We use dataof monthly house prices from the larger Stockholm area and the municipality of Uppsalabetween 2005 and early 2019 as the time series to be forecast. Firstly, we compare theperformance of two machine learning methods, the Long Short-Term Memory, and theSupport Vector Machine methods. The two methods forecasts are compared, and themodel with the lowest forecasting error measured by three metrics is chosen to be comparedwith a classic seasonal ARIMA model. We find that the Long Short-Term Memorymethod is the better performing machine learning method for a twelve-month forecast,but that it still does not forecast as well as the ARIMA model for the same forecast period.
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Murugan, Srikala. "Determining Event Outcomes from Social Media." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703427/.
Full textAbstract:
An event is something that happens at a time and location. Events include major life events such as graduating college or getting married, and also simple day-to-day activities such as commuting to work or eating lunch. Most work on event extraction detects events and the entities involved in events. For example, cooking events will usually involve a cook, some utensils and appliances, and a final product. In this work, we target the task of determining whether events result in their expected outcomes. Specifically, we target cooking and baking events, and characterize event outcomes into two categories. First, we distinguish whether something edible resulted from the event. Second, if something edible resulted, we distinguish between perfect, partial and alternative outcomes. The main contributions of this thesis are a corpus of 4,000 tweets annotated with event outcome information and experimental results showing that the task can be automated. The corpus includes tweets that have only text as well as tweets that have text and an image.
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Sonnert, Adrian. "Predicting inter-frequency measurements in an LTE network using supervised machine learning : a comparative study of learning algorithms and data processing techniques." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148553.
Full textAbstract:
With increasing demands on network reliability and speed, network suppliers need to effectivize their communications algorithms. Frequency measurements are a core part of mobile network communications, increasing their effectiveness would increase the effectiveness of many network processes such as handovers, load balancing, and carrier aggregation. This study examines the possibility of using supervised learning to predict the signal of inter-frequency measurements by investigating various learning algorithms and pre-processing techniques. We found that random forests have the highest predictive performance on this data set, at 90.7\% accuracy. In addition, we have shown that undersampling and varying the discriminator are effective techniques for increasing the performance on the positive class on frequencies where the negative class is prevalent. Finally, we present hybrid algorithms in which the learning algorithm for each model depends on attributes of the training data set. These algorithms perform at a much higher efficiency in terms of memory and run-time without heavily sacrificing predictive performance.
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Kondamari, Pramod Sai, and Anudeep Itha. "A Deep Learning Application for Traffic Sign Recognition." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21890.
Full textAbstract:
Background: Traffic Sign Recognition (TSR) is particularly useful for novice driversand self-driving cars. Driver Assistance Systems(DAS) involves automatic trafficsign recognition. Efficient classification of the traffic signs is required in DAS andunmanned vehicles for safe navigation. Convolutional Neural Networks(CNN) isknown for establishing promising results in the field of image classification, whichinspired us to employ this technique in our thesis. Computer vision is a process thatis used to understand the images and retrieve data from them. OpenCV is a Pythonlibrary used to detect traffic sign images in real-time. Objectives: This study deals with an experiment to build a CNN model which canclassify the traffic signs in real-time effectively using OpenCV. The model is builtwith low computational cost. The study also includes an experiment where variouscombinations of parameters are tuned to improve the model’s performance. Methods: The experimentation method involve building a CNN model based onmodified LeNet architecture with four convolutional layers, two max-pooling layersand two dense layers. The model is trained and tested with the German Traffic SignRecognition Benchmark (GTSRB) dataset. Parameter tuning with different combinationsof learning rate and epochs is done to improve the model’s performance.Later this model is used to classify the images introduced to the camera in real-time. Results: The graphs depicting the accuracy and loss of the model before and afterparameter tuning are presented. An experiment is done to classify the traffic signimage introduced to the camera by using the CNN model. High probability scoresare achieved during the process which is presented. Conclusions: The results show that the proposed model achieved 95% model accuracywith an optimum number of epochs, i.e., 30 and default optimum value oflearning rate, i.e., 0.001. High probabilities, i.e., above 75%, were achieved when themodel was tested using new real-time data.
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Holm, Rasmus. "Prediction of Inter-Frequency Measurements in a LTE Network with Deep Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151879.
Full textAbstract:
The telecommunications industry faces difficult challenges as more and more devices communicate over the internet. A telecommunications network is a complex system with many parts and some are candidates for further automation. We have focused on interfrequency measurements that are used during inter-frequency handovers, among other procedures. A handover is the procedure when for instance a phone changes the base station it communicates with and the inter-frequency measurements are rather expensive to perform. More specifically, we have investigated the possibility of using deep learning—an ever expanding field in machine learning—for predicting inter-frequency measurements in a Long Term Evolution (LTE) network. We have focused on the multi-layer perceptron and extended it with (variational) autoencoders or modified it through dropout such that it approximate the predictive distribution of a Gaussian process. The telecommunications network consist of many cells and each cell gather its own data. One of the strengths of deep learning models is that they usually increase their performance as more and more data is used. We have investigated whether we do see an increase in performance if we combine data from multiple cells and the results show that this is not necessarily the case. The performances are comparable between models trained on combined data from multiple cells and models trained on data from individual cells. We can expect the multi-layer perceptron to perform better than a linear regression model. The best performing multi-layer perceptron architectures have been rather shallow, 1-2 hidden layers, and the extensions/modifications we have used/done have not shown any significant improvements to warrant their presence. For the particular LTE network we have worked with we would recommend to use shallow multi-layer perceptron architectures as far as deep learning models are concerned.
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Gomes, Leonaldo da Silva. "Redes Neurais Aplicadas à InferÃncia dos Sinais de Controle de Dosagem de Coagulantes em uma ETA por FiltraÃÃo RÃpida." Universidade Federal do CearÃ, 2012. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=8105.
Full textAbstract:
Considerando a importÃncia do controle da coagulaÃÃo quÃmica para o processo de
tratamento de Ãgua por filtraÃÃo rÃpida, esta dissertaÃÃo propÃe a aplicaÃÃo de redes neurais
artificiais para inferÃncia dos sinais de controle de dosagem de coagulantes principal e
auxiliar, no processo de coagulaÃÃo quÃmica em uma estaÃÃo de tratamento de Ãgua por
filtraÃÃo rÃpida. Para tanto, foi feito uma anÃlise comparativa da aplicaÃÃo de modelos
baseados em redes neurais do tipo: alimentada adiante focada atrasada no tempo (FTLFN);
alimentada adiante atrasada no tempo distribuÃda (DTLFN); recorrente de Elman (ERN) e
auto-regressiva nÃo-linear com entradas exÃgenas (NARX). Da anÃlise comparativa, o
modelo baseado em redes NARX apresentou melhores resultados, evidenciando o potencial
do modelo para uso em casos reais, o que contribuirà para a viabilizaÃÃo de projetos desta
natureza em estaÃÃes de tratamento de Ãgua de pequeno porte.Considering the importance of the chemical coagulation control for the water treatment by direct filtration, this work proposes the application of artificial neural networks for inference of dosage control signals of principal and auxiliary coagulant, in the chemical coagulation process in a water treatment plant by direct filtration. To that end, was made a comparative analysis of the application of models based on neural networks, such as: Focused Time Lagged Feedforward Network (FTLFN); Distributed Time Lagged Feedforward Network (DTLFN); Elman Recurrent Network (ERN) and Non-linear Autoregressive with exogenous inputs (NARX). From the comparative analysis, the model based on NARX networks showed better results, demonstrating the potential of the model for use in real cases, which will contribute to the viability of projects of this nature in small size water treatment plants.
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Schennings, Jacob. "Deep Convolutional Neural Networks for Real-Time Single Frame Monocular Depth Estimation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-336923.
Full textAbstract:
Vision based active safety systems have become more frequently occurring in modern vehicles to estimate depth of the objects ahead and for autonomous driving (AD) and advanced driver-assistance systems (ADAS). In this thesis a lightweight deep convolutional neural network performing real-time depth estimation on single monocular images is implemented and evaluated. Many of the vision based automatic brake systems in modern vehicles only detect pre-trained object types such as pedestrians and vehicles. These systems fail to detect general objects such as road debris and roadside obstacles. In stereo vision systems the problem is resolved by calculating a disparity image from the stereo image pair to extract depth information. The distance to an object can also be determined using radar and LiDAR systems. By using this depth information the system performs necessary actions to avoid collisions with objects that are determined to be too close. However, these systems are also more expensive than a regular mono camera system and are therefore not very common in the average consumer car. By implementing robust depth estimation in mono vision systems the benefits from active safety systems could be utilized by a larger segment of the vehicle fleet. This could drastically reduce human error related traffic accidents and possibly save many lives. The network architecture evaluated in this thesis is more lightweight than other CNN architectures previously used for monocular depth estimation. The proposed architecture is therefore preferable to use on computationally lightweight systems. The network solves a supervised regression problem during the training procedure in order to produce a pixel-wise depth estimation map. The network was trained using a sparse ground truth image with spatially incoherent and discontinuous data and output a dense spatially coherent and continuous depth map prediction. The spatially incoherent ground truth posed a problem of discontinuity that was addressed by a masked loss function with regularization. The network was able to predict a dense depth estimation on the KITTI dataset with close to state-of-the-art performance.
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Evangelisti, Davide. "RL-UniBOt: Applicazione di tecniche di Reinforcement Learning al gioco Rocket League." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
Find full textAbstract:
L'applicazione dell'intelligenza artificiale nell'ambito videoludico al fine di creare bot intelligenti è, nella maggior parte dei casi riportati in letteratura, circoscritto a videogiochi dalla complessità limitata.
In questo lavoro di tesi si propone lo studio e l'utilizzo di diverse tecniche di Deep Learning per l'addestramento di Artificial Intelligence in grado di giocare a Rocket League.
Partendo dall'analisi dettagliata del gioco e sfruttando gli algoritmi allo stato dell'arte del Deep Learning, si presentano molteplici soluzioni studiate appositamente per l'addestramento di bot in grado di comprendere l'ambiente e giocare partite uno contro uno.
Si propongono, inoltre, confronti e considerazioni sulle tecniche utilizzate allo scopo di evidenziare pregi e difetti delle soluzioni proposte.
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Nyman, David. "Injector diagnosis based on engine angular velocity pulse pattern recognition." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414967.
Full textAbstract:
In a modern diesel engine, a fuel injector is a vital component. The injectors control the fuel dosing into the combustion chambers. The accuracy in the fuel dosing is very important as inaccuracies have negative effects on engine out emissions and the controllability. Because of this, a diagnosis that can classify the conditions of the injectors with good accuracy is highly desired. A signal that contains information about the injectors condition, is the engine angular velocity. In this thesis, the classification performance of six common machine learning methods is evaluated. The input to the methods is the engine angular velocity. In addition to the classification performance, also the computational cost of the methods, in a deployed state, is analysed. The methods are evaluated on data from a Scania truck that has been run just like any similar commercial vehicle. The six methods evaluated are: logistic regression, kernel logistic regression, linear discriminant analysis, quadratic discriminant analysis, fully connected neural networks and, convolutional neural networks. The results show that the neural networks achieve the best classification performance. Furthermore, the neural networks also achieve the best classification performance from a, in a deployed state, computational cost effectiveness perspective. Results also indicate that the neural networks can avoid false alarms and maintain high sensitivity.
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Lin, Fanqing. "Flow Adaptive Video Object Segmentation." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7067.
Full textAbstract:
We tackle the task of semi-supervised video object segmentation, i.e, pixel-level object classification of the images in video sequences using very limited ground truth training data of its corresponding video. Recently introduced online adaptation of convolutional neural networks for video object segmentation (OnAVOS) has achieved good results by pretraining the network, fine-tuning on the first frame and training the network at test time using its approximate prediction as newly obtained ground truth. We propose Flow Adaptive Video Object Segmentation (FAVOS) that refines the generated adaptive ground truth for online updates and utilizes temporal consistency between video frames with the help of optical flow. We validate our approach on the DAVIS Challenge and achieve rank 1 results on the DAVIS 2016 Challenge (single-object segmentation) and competitive scores on both DAVIS 2018 Semi-supervised Challenge and Interactive Challenge (multi-object segmentation). While most models tend to have increasing complexity for the challenging task of video object segmentation, FAVOS provides a simple and efficient pipeline that produces accurate predictions.
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Barkman, Richard Dan William. "Object Tracking Achieved by Implementing Predictive Methods with Static Object Detectors Trained on the Single Shot Detector Inception V2 Network." Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-73313.
Full textAbstract:
In this work, the possibility of realising object tracking by implementing predictive methods with static object detectors is explored. The static object detectors are obtained as models trained on a machine learning algorithm, or in other words, a deep neural network. Specifically, it is the single shot detector inception v2 network that will be used to train such models. Predictive methods will be incorporated to the end of improving the obtained models’ precision, i.e. their performance with respect to accuracy. Namely, Lagrangian mechanics will be employed to derived equations of motion for three different scenarios in which the object is to be tracked. These equations of motion will be implemented as predictive methods by discretising and combining them with four different iterative formulae. In ch. 1, the fundamentals of supervised machine learning, neural networks, convolutional neural networks as well as the workings of the single shot detector algorithm, approaches to hyperparameter optimisation and other relevant theory is established. This includes derivations of the relevant equations of motion and the iterative formulae with which they were implemented. In ch. 2, the experimental set-up that was utilised during data collection, and the manner by which the acquired data was used to produce training, validation and test datasets is described. This is followed by a description of how the approach of random search was used to train 64 models on 300×300 datasets, and 32 models on 512×512 datasets. Consecutively, these models are evaluated based on their performance with respect to camera-to-object distance and object velocity. In ch. 3, the trained models were verified to possess multi-scale detection capabilities, as is characteristic of models trained on the single shot detector network. While the former is found to be true irrespective of the resolution-setting of the dataset that the model has been trained on, it is found that the performance with respect to varying object velocity is significantly more consistent for the lower resolution models as they operate at a higher detection rate. Ch. 3 continues with that the implemented predictive methods are evaluated. This is done by comparing the resulting deviations when they are let to predict the missing data points from a collected detection pattern, with varying sampling percentages. It is found that the best predictive methods are those that make use of the least amount of previous data points. This followed from that the data upon which evaluations were made contained an unreasonable amount of noise, considering that the iterative formulae implemented do not take noise into account. Moreover, the lower resolution models were found to benefit more than those trained on the higher resolution datasets because of the higher detection frequency they can employ. In ch. 4, it is argued that the concept of combining predictive methods with static object detectors to the end of obtaining an object tracker is promising. Moreover, the models obtained on the single shot detector network are concluded to be good candidates for such applications. However, the predictive methods studied in this thesis should be replaced with some method that can account for noise, or be extended to be able to account for it. A profound finding is that the single shot detector inception v2 models trained on a low-resolution dataset were found to outperform those trained on a high-resolution dataset in certain regards due to the higher detection rate possible on lower resolution frames. Namely, in performance with respect to object velocity and in that predictive methods performed better on the low-resolution models.I detta arbete undersöks möjligheten att åstadkomma objektefterföljning genom att implementera prediktiva metoder med statiska objektdetektorer. De statiska objektdetektorerna erhålls som modeller tränade på en maskininlärnings-algoritm, det vill säga djupa neurala nätverk. Specifikt så är det en modifierad version av entagningsdetektor-nätverket, så kallat entagningsdetektor inception v2 nätverket, som används för att träna modellerna. Prediktiva metoder inkorporeras sedan för att förbättra modellernas förmåga att kunna finna ett eftersökt objekt. Nämligen används Lagrangiansk mekanik för härleda rörelseekvationer för vissa scenarion i vilka objektet är tänkt att efterföljas. Rörelseekvationerna implementeras genom att låta diskretisera dem och därefter kombinera dem med fyra olika iterationsformler. I kap. 2 behandlas grundläggande teori för övervakad maskininlärning, neurala nätverk, faltande neurala nätverk men också de grundläggande principer för entagningsdetektor-nätverket, närmanden till hyperparameter-optimering och övrig relevant teori. Detta inkluderar härledningar av rörelseekvationerna och de iterationsformler som de skall kombineras med. I kap. 3 så redogörs för den experimentella uppställning som användes vid datainsamling samt hur denna data användes för att producera olika data set. Därefter följer en skildring av hur random search kunde användas för att träna 64 modeller på data av upplösning 300×300 och 32 modeller på data av upplösning 512×512. Vidare utvärderades modellerna med avseende på deras prestanda för varierande kamera-till-objekt avstånd och objekthastighet. I kap. 4 så verifieras det att modellerna har en förmåga att detektera på flera skalor, vilket är ett karaktäristiskt drag för modeller tränade på entagninsdetektor-nätverk. Medan detta gällde för de tränade modellerna oavsett vilken upplösning av data de blivit tränade på, så fanns detekteringsprestandan med avseende på objekthastighet vara betydligt mer konsekvent för modellerna som tränats på data av lägre upplösning. Detta resulterade av att dessa modeller kan arbeta med en högre detekteringsfrekvens. Kap. 4 fortsätter med att de prediktiva metoderna utvärderas, vilket de kunde göras genom att jämföra den resulterande avvikelsen de respektive metoderna innebar då de läts arbeta på ett samplat detektionsmönster, sparat från då en tränad modell körts. I och med denna utvärdering så testades modellerna för olika samplingsgrader. Det visade sig att de bästa iterationsformlerna var de som byggde på färre tidigare datapunkter. Anledningen för detta är att den insamlade data, som testerna utfördes på, innehöll en avsevärd mängd brus. Med tanke på att de implementerade iterationsformlerna inte tar hänsyn till brus, så fick detta avgörande konsekvenser. Det fanns även att alla prediktiva metoder förbättrade objektdetekteringsförmågan till en högre utsträckning för modellerna som var tränade på data av lägre upplösning, vilket följer från att de kan arbeta med en högre detekteringsfrekvens. I kap. 5, argumenteras det, bland annat, för att konceptet att kombinera prediktiva metoder med statiska objektdetektorer för att åstadkomma objektefterföljning är lovande. Det slutleds även att modeller som erhålls från entagningsdetektor-nätverket är lovande kandidater för detta applikationsområde, till följd av deras höga detekteringsfrekvenser och förmåga att kunna detektera på flera skalor. Metoderna som användes för att förutsäga det efterföljda föremålets position fanns vara odugliga på grund av deras oförmåga att kunna hantera brus. Det slutleddes därmed att dessa antingen bör utökas till att kunna hantera brus eller ersättas av lämpligare metoder. Den mest väsentliga slutsats detta arbete presenterar är att lågupplösta entagninsdetektormodeller utgör bättre kandidater än de tränade på data av högre upplösning till följd av den ökade detekteringsfrekvens de erbjuder.
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Siddique, Nahian A. "PATTERN RECOGNITION IN CLASS IMBALANCED DATASETS." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4480.
Full textAbstract:
Class imbalanced datasets constitute a significant portion of the machine learning problems of interest, where recognizing the ‘rare class’ is the primary objective for most applications. Traditional linear machine learning algorithms are often not effective in recognizing the rare class. In this research work, a specifically optimized feed-forward artificial neural network (ANN) is proposed and developed to train from moderate to highly imbalanced datasets.
The proposed methodology deals with the difficulty in classification task in multiple stages—by optimizing the training dataset, modifying kernel function to generate the gram matrix and optimizing the NN structure. First, the training dataset is extracted from the available sample set through an iterative process of selective under-sampling. Then, the proposed artificial NN comprises of a kernel function optimizer to specifically enhance class boundaries for imbalanced datasets by conformally transforming the kernel functions. Finally, a single hidden layer weighted neural network structure is proposed to train models from the imbalanced dataset. The proposed NN architecture is derived to effectively classify any binary dataset with even very high imbalance ratio with appropriate parameter tuning and sufficient number of processing elements.
Effectiveness of the proposed method is tested on accuracy based performance metrics, achieving close to and above 90%, with several imbalanced datasets of generic nature and compared with state of the art methods. The proposed model is also used for classification of a 25GB computed tomographic colonography database to test its applicability for big data. Also the effectiveness of under-sampling, kernel optimization for training of the NN model from the modified kernel gram matrix representing the imbalanced data distribution is analyzed experimentally. Computation time analysis shows the feasibility of the system for practical purposes. This report is concluded with discussion of prospect of the developed model and suggestion for further development works in this direction.
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Fongo, Daniele. "Previsione del declino funzionale tramite l'utilizzo di Reti Neurali Ricorrenti." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14889/.
Full textAbstract:
PreventIT è un progetto europeo nato con il fine di prevenire il declino funzionale e l'insorgere di disabilità in persone prossime all'anzianità. Questo viene fatto da una parte promuovendo un invecchiamento salutare attraverso l'uso di dispositivi tecnologici come smartphone e smartwatch che incoraggino l'attività fisica, dall'altra parte effettuando degli screening continui, osservazioni o controlli periodici per analizzare i rischi del declino ed individuare le persone più in pericolo al fine di intervenire il prima possibile.
A partire dagli stessi dati utilizzati all'interno del progetto europeo sopra citato, lo scopo della tesi è stato quello di sviluppare un tool parallelo basato su Reti Neurali Artificiali in grado di automatizzare tale analisi del rischio, offrendo una buona previsione del possibile declino funzionale futuro a partire da una serie di osservazioni sulle persone. In particolare, l'interesse scientifico del progetto è stato nel constatare quale fosse il modello di rete neurale che meglio riuscisse a predire una classe di rischio partendo da uno scenario complesso in cui le osservazioni risultano eterogenee poiché estrapolate da multipli dataset differenti.
I risultati sperimentali dimostrano che l’utilizzo di Long Short-Term Memory e di Deep Feedforward garantiscono ottime previsioni di declino funzionale, con un AUCROC pari a 0.881 e 0.883 rispettivamente. Ciò indica che un approccio ricorrente ed un’analisi temporale di intere sequenze di osservazioni potrebbero non risultare necessari per la predizione del declino funzionale.
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Velander, Alice, and Harrysson David Gumpert. "Do Judge a Book by its Cover! : Predicting the genre of book covers using supervised deep learning. Analyzing the model predictions using explanatory artificial intelligence methods and techniques." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177691.
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In Storytel’s application on which a user can read and listen to digitalized literature, a user is displayed a list of books where the first thing the user encounters is the book title and cover. A book cover is therefore essential to attract a consumer’s attention. In this study, we take a data-driven approach to investigate the design principles for book covers through deep learning models and explainable AI. The first aim is to explore how well a Convolutional Neural Network (CNN) can interpret and classify a book cover image according to its genre in a multi-class classification task. The second aim is to increase model interpretability and investigate model feature to genre correlations. With the help of the explanatory artificial intelligence method Gradient-weighted Class Activation Map (Grad-CAM), we analyze the pixel-wise contribution to the model prediction. In addition, object detection by YOLOv3 was implemented to investigate which objects are detectable and reoccurring in the book covers. An interplay between Grad-CAM and YOLOv3 was used to investigate how identified objects and features correlate to a specific book genre and ultimately answer what makes a good book cover. Using a State-of-the-Art CNN model architecture we achieve an accuracy of 48% with the best class-wise accuracies for genres Erotica, Economy & Business and Children with accuracies 73%, 67% and 66%. Quantitative results from the Grad-CAM and YOLOv3 interplay show some strong associations between objects and genres, while indicating weak associations between abstract design principles and genres. Furthermore, a qualitative analysis of Grad-CAM visualizations show strong relevance of certain objects and text fonts for specific book genres. It was also observed that the portrayal of a feature was relevant for the model prediction of certain genres.
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Santos, Rosiane Correia. "LearnInPlanner: uma abordagem de aprendizado supervisionado com redes neurais para solução de problemas de planejamento clássico." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-25012014-115621/.
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A busca progressiva no espaço de estados é uma das abordagens mais populares de Planejamento Automatizado. O desempenho dos algoritmos de busca progressiva é influenciado pela heurística independente de domínio utilizada para guiá-lo. Nesse contexto, o foco do presente trabalho consiste em investigar técnicas de aprendizado de máquina supervisionadas que possibilitaram agregar à heurística do plano relaxado, comumente utilizada em abordagens atuais de planejamento, informações sobre o domínio em questão que viessem a ser úteis ao algoritmo de busca. Essas informações foram representadas por meio de um espaço de características do problema de planejamento e uma rede neural MLP foi aplicada para estimar uma nova função heurística para guiar a busca por meio de um processo de regressão não linear. Uma vez que o conjunto de características disponíveis para a construção da nova função heurística é grande, foi necessário a definição de um processo de seleção de características capaz de determinar qual conjunto de características de entrada da rede resultaria em melhor desempenho para o modelo de regressão. Portanto, para a seleção de características, aplicou-se uma abordagem de algoritmos genéticos. Como principal resultado, tem-se uma análise comparativa do desempenho entre a utilização da heurística proposta neste trabalho e a utilização da heurística do plano relaxado para guiar o algoritmo de busca na tarefa de planejamento. Para a análise empírica foram utilizados domínios de diferentes complexidades disponibilizados pela Competições Internacionais de Planejamento. Além dos resultados empíricos e análises comparativas, as contribuições deste trabalho envolvem o desenvolvimento de um novo planejador independente de domínio, denominado LearnInPlanner. Esse planejador utiliza a nova função heurística estimada por meio do processo de aprendizado e o algoritmo de Busca Gulosa para solucionar os problemas de planejamento.The forward state-space search is one of the most popular Automated Planning approaches. The performance of forward search algorithms is affected by the domain-independent heuristic being used. In this context, the focus of this work consisted on investigating techniques of supervised machine learning that make possible to agregate to the relaxed plan heuristic, commonly used in current planning approaches, information about the domain which could be useful to the search algorithm. This information has been represented through a feature space of planning problem and a MLP neural network has been applied to estimate a new heuristic function for guiding the search through a non-linear regression process. Once the set of features available for the construction of the new heuristic function is large, it was necessary to define a feature selection process capable of determining which set of neural network input features would result in the best performance for the regression model. Therefore, for selecting features, an approach of genetic algorithms has been applied. As the main result, one has obtained a comparative performance analysis between the use of heuristic proposed in this work and the use of the relaxed plan heuristic to guide the search algorithm in the planning task. For the empirical analysis were used domains with different complexities provided by the International Planning Competitions. In addition to the empirical results and comparative analysis, the contributions of this work involves the development of a new domain-independent planner, named LearnInPlanner. This planner uses the new heuristic function estimated by the learning process and the Greedy Best-First search algorithm to solve planning problems.
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Golshan, Arman. "A contemporary machine learning approach to detect transportation mode - A case study of Borlänge, Sweden." Thesis, Högskolan Dalarna, Mikrodataanalys, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-35966.
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Understanding travel behavior and identifying the mode of transportation are essential for adequate urban devising and transportation planning. Global positioning systems (GPS) tracking data is mainly used to find human mobility patterns in cities. Some travel information, such as most visited location, temporal changes, and the trip speed, can be easily extracted from GPS raw tracking data. GPS trajectories can be used as a method to indicate the mobility modes of commuters. Most previous studies have applied traditional machine learning algorithms and manually computed data features, making the model error-prone. Thus, there is a demand for developing a new model to resolve these methods' weaknesses. The primary purpose of this study is to propose a semi-supervised model to identify transportation mode by using a contemporary machine learning algorithm and GPS tracking data. The model can accept GPS trajectory with adjustable length and extracts their latent information with LSTM Autoencoder. This study adopts a deep neural network architecture with three hidden layers to map the latent information to detect transportation mode. Moreover, different case studies are performed to evaluate the proposed model's efficiency. The model results in an accuracy of 93.6%, which significantly outperforms similar studies.
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Nyström, Jonatan. "Identifying Units on a WiFi Based on Their Physical Properties." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254255.
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This project aims to classify different units on a wireless network with the use of their frequency response. This is in purpose to increase security when communicating over WiFi. We use a convolution neural network for finding symmetries in the frequency responses recorded from two different units. We used two pre-recorded sets of data which contained the same units but from two different locations. The project achieve an accuracy of 99.987%, with a 5 hidden layers CNN, when training and testing on one dataset. When training the neural network on one set and testing it on a second set, we achieve results below 54.12% for identifying the units. At the end we conclude that the amount of data needed, for achieving high enough accuracy, is to large for this method to be a practical solution for non-stationary units.
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Roychowdhury, Soumali. "Supervised and Semi-Supervised Learning in Vision using Deep Neural Networks." Thesis, IMT Alti Studi Lucca, 2019. http://e-theses.imtlucca.it/273/1/Roychowdhury_phdthesis.pdf.
Full textAbstract:
Deep learning has been a huge success in different vision
tasks like classification, object detection, segmentation etc.,
allowing to start from raw pixels to integrated deep neural models. This thesis aims to solve some of these vision
problems using several deep neural network architectures
in different ways. The first and the core part of this thesis
focuses on a learning framework that extends the previous
work on Semantic Based Regularization (SBR) to integrate
prior knowledge into deep learners. Deep neural networks
are empirical learners and therefore heavily depend on labeled examples, whereas knowledge based learners on the
other hand are not very efficient in solving complex vision
problems. Therefore, SBR is designed as a semi-supervised
framework that can tightly integrate empirical learners with
any available background knowledge to get the advantages
of learning from both perception and reasoning/knowledge.
The framework is learner agnostic and any learning machinery can be used. In the earlier works of SBR, kernel machines
or shallow networks were used as learners. The approach
of the problem, concept of using multi-task logic functions
are borrowed form the previous works of SBR. But for the
first time, in this research work, the integration of logic constraints is done with deep neural networks. The thesis defines a novel back propagation schema for optimization of
deep neural networks in SBR and also uses several heuristics to integrate convex and concave logic constraints into
the deep learners. It also focuses on extensive experimental evaluations performed on multiple image classification
datasets to show how the integration of the prior knowledge
in deep learners can be used to boost the accuracy of several neural architectures over their individual counterparts.
SBR is also used in a video classification problem to automatically annotate surgical and non-surgical tools from videos of
cataracts surgery. This framework achieves a high accuracy compared to the human annotators and the state-of-the-art
DResSys by enforcing temporal consistency among the consecutive video frames using prior knowledge in deep neural networks through collective classification during the inference time. DResSys, an ensemble of deep convolutional
neural networks and a Markov Random Field based framework (CNN-MRF) is used, whereas SBR replaces the MRF
graph with logical constraints for enforcing a regularization
in the temporal domain. Therefore, SBR and DResSys, two
deep learning based frameworks discussed in this thesis, are
able to distill prior knowledge into deep neural networks and
hence become useful tools for decision support during interoperative cataract surgeries, in report generation, in surgical training etc. Therefore, the first part of the thesis designs scientific frameworks that enable exploiting the wealth
of domain knowledge and integrate it with deep convolutional neural networks for solving many real world vision
problems and can be used in several industrial applications.
In the present world, a range of different businesses possess huge databases with visuals which are difficult to manage and make use of. Since they may not have an effective
method to make sense of all the visual data, it might end up
uncategorized and useless. If a visual database does not contain meta data about the images or videos, categorizing it, is
a huge hassle. Classification of images and videos through
useful domain information using these unified frameworks
like SBR is a key solution. The second part of the thesis focuses on another vision problem of image segmentation and
this part of the thesis is more application-specific. However,
it can still be viewed as utilizing some universal and basic
domain knowledge techniques with deep learning models.
It designs two deep learning based frameworks and makes
a head to head comparison of the two approaches in terms
of speed, efficiency and cost. The frameworks are built for
automatic segmentation and classification of contaminants
for cleanliness analysis in automobile, aerospace or manufacturing industries. The frameworks are designed to meet the
foremost industry requirement of having an end-to-end solution that is cheap, reliable, fast and accurate in comparison
to the traditional techniques presently used in the contaminant analysis and quality control process. These end-to-end
solutions when integrated with the simple optical acquisition
systems, will help in replacing the expensive slow systems
presently existing in the market.
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Sporea, Ioana. "Supervised learning in multilayer spiking neural networks." Thesis, University of Surrey, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.576119.
Full textAbstract:
In this thesis, a new supervised learning algorithm for multilayer spik- ing neural networks is presented. Gradient descent learning algo- rithms have led traditional neural networks with multiple layers to be one of the most powerful and flexible computational models derived from artificial neural networks. However, more recent experimental evidence suggests that biological neural systems use the exact time of single action potentials to encode information. These findings have led to a new way of simulating neural networks based on temporal en- coding with single spikes. Analytical demonstrations show that these types of neural networks are computationally more powerful than net- works of rate neurons. Conversely, the existing learning algorithms no longer apply to spik- ing neural networks. Supervised learning algorithms based on gradient descent, such as SpikeProp and its extensions, have been developed for spiking neural networks with multiple layers, but these ate limited to a specific model of neurons, with only the first spike being consid- ered. Another learning algorithm, ReSuMe, for single layer networks is based on spike-timing dependent plasticity ~STDP) and uses the computational power of multiple spikes; moreover, this algorithm is not limited to a specific neuron model. The algorithm presented here is based on the gradient descent method, while making use of STDP and can be applied to networks with multi- ple layers. Furthermore, the algorithm is not limited to neurons firing single spikes or specific neuron models. Results on classic benchmarks, such as the XOR problem and the Iris data set, show that the algo- rithm is capable of non-linear transformations. Complex classification tasks have also been applied with fast convergence times. The results of the simulations show that the new learning rule is as efficient as SpikeProp while having all the advantages of STDP. The supervised learning algorithm for spiking neurons is compared with the back- propagation algorithm for rate neurons by modelling an audio-visual perceptual illusion, the McGurk effect.
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Wang, Yuxuan. "Supervised Speech Separation Using Deep Neural Networks." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1426366690.
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Graves, Alex. "Supervised sequence labelling with recurrent neural networks." kostenfrei, 2008. http://mediatum2.ub.tum.de/doc/673554/673554.pdf.
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Bodén, Johan. "A Comparative Study of Reinforcement-based and Semi-classical Learning in Sensor Fusion." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-84784.
Full textAbstract:
Reinforcement learning has proven itself very useful in certain areas, such as games. However, the approach has been seen as quite limited. Reinforcement-based learning has for instance not been commonly used for classification tasks as it is receiving feedback on how well it did for an action performed on a specific input. This slows the performance convergence rate as compared to other classification approaches which has the input and the corresponding output to train on. Nevertheless, this thesis aims to investigate whether reinforcement-based learning could successfully be employed on a classification task. Moreover, as sensor fusion is an expanding field which can for instance assist autonomous vehicles in understanding its surroundings, it is also interesting to see how sensor fusion, i.e., fusion between lidar and RGB images, could increase the performance in a classification task. In this thesis, a reinforcement-based learning approach is compared to a semi-classical approach. As an example of a reinforcement learning model, a deep Q-learning network was chosen, and a support vector machine classifier built on top of a deep neural network, was chosen as an example of a semi-classical model. In this work, these frameworks are compared with and without sensor fusion to see whether fusion improves their performance. Experiments show that the evaluated reinforcement-based learning approach underperforms in terms of metrics but mainly due to its slow learning process, in comparison to the semi-classical approach. However, on the other hand using reinforcement-based learning to carry out a classification task could still in some cases be advantageous, as it still performs fairly well in terms of the metrics presented in this work, e.g. F1-score, or for instance imbalanced datasets. As for the impact of sensor fusion, a notable improvement can be seen, e.g. when training the deep Q-learning model for 50 episodes, the F1-score increased with 0.1329; especially, when taking into account that the most of the lidar data used in the fusion is lost since this work projects the 3D lidar data onto the same 2D plane as the RGB images.
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BORDONE, MOLINI ANDREA. "Deep learning for inverse problems in remote sensing: super-resolution and SAR despeckling." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2903492.
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Le, Lan Gaël. "Analyse en locuteurs de collections de documents multimédia." Thesis, Le Mans, 2017. http://www.theses.fr/2017LEMA1020/document.
Full textAbstract:
La segmentation et regroupement en locuteurs (SRL) de collection cherche à répondre à la question « qui parle quand ? » dans une collection de documents multimédia. C’est un prérequis indispensable à l’indexation des contenus audiovisuels. La tâche de SRL consiste d’abord à segmenter chaque document en locuteurs, avant de les regrouper à l'échelle de la collection. Le but est de positionner des labels anonymes identifiant les locuteurs, y compris ceux apparaissant dans plusieurs documents, sans connaître à l'avance ni leur identité ni leur nombre. La difficulté posée par le regroupement en locuteurs à l'échelle d'une collection est le problème de la variabilité intra-locuteur/inter-document : selon les documents, un locuteur peut parler dans des environnements acoustiques variés (en studio, dans la rue...). Cette thèse propose deux méthodes pour pallier le problème. D'une part, une nouvelle méthode de compensation neuronale de variabilité est proposée, utilisant le paradigme de triplet-loss pour son apprentissage. D’autre part, un procédé itératif d'adaptation non supervisée au domaine est présenté, exploitant l'information, même imparfaite, que le système acquiert en traitant des données, pour améliorer ses performances sur le domaine acoustique cible. De plus, de nouvelles méthodes d'analyse en locuteurs des résultats de SRL sont étudiées, pour comprendre le fonctionnement réel des systèmes, au-delà du classique taux d'erreur de SRL (Diarization Error Rate ou DER). Les systèmes et méthodes sont évalués sur deux émissions télévisées d'une quarantaine d'épisodes, pour les architectures de SRL globale ou incrémentale, à l'aide de la modélisation locuteur à l'état de l'artThe task of speaker diarization and linking aims at answering the question "who speaks and when?" in a collection of multimedia recordings. It is an essential step to index audiovisual contents. The task of speaker diarization and linking firstly consists in segmenting each recording in terms of speakers, before linking them across the collection. Aim is, to identify each speaker with a unique anonymous label, even for speakers appearing in multiple recordings, without any knowledge of their identity or number. The challenge of the cross-recording linking is the modeling of the within-speaker/across-recording variability: depending on the recording, a same speaker can appear in multiple acoustic conditions (in a studio, in the street...). The thesis proposes two methods to overcome this issue. Firstly, a novel neural variability compensation method is proposed, using the triplet-loss paradigm for training. Secondly, an iterative unsupervised domain adaptation process is presented, in which the system exploits the information (even inaccurate) about the data it processes, to enhance its performances on the target acoustic domain. Moreover, novel ways of analyzing the results in terms of speaker are explored, to understand the actual performance of a diarization and linking system, beyond the well-known Diarization Error Rate (DER). Systems and methods are evaluated on two TV shows of about 40 episodes, using either a global, or longitudinal linking architecture, and state of the art speaker modeling (i-vector)
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Buttar, Sarpreet Singh. "Applying Artificial Neural Networks to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87117.
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Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation space is small. The size of the adaptation space is often in hundreds or thousands, which makes formal analysis approaches inefficient in large-scale self-adaptive systems. In this thesis, we tackle this problem by presenting an online learning approach that enables formal analysis approaches to analyze large adaptation spaces efficiently. The approach integrates with the standard feedback loop and reduces the adaptation space to a subset of adaptation options that are relevant to the current runtime uncertainties. The subset is then analyzed by the formal analysis approaches, which allows them to complete the analysis faster and efficiently within the available time. We evaluate our approach on two different instances of an Internet of Things application. The evaluation shows that our approach dramatically reduces the adaptation space and analysis time without compromising the adaptation goals.
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Hu, Renjie. "Random neural networks for dimensionality reduction and regularized supervised learning." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/6960.
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This dissertation explores Random Neural Networks (RNNs) in several aspects and their applications. First, Novel RNNs have been proposed for dimensionality reduction and visualization. Based on Extreme Learning Machines (ELMs) and Self-Organizing Maps (SOMs) a new method is created to identify the important variables and visualize the data. This technique reduces the curse of dimensionality and improves furthermore the interpretability of the visualization and is tested on real nursing survey datasets. ELM-SOM+ is an autoencoder created to preserves the intrinsic quality of SOM and also brings continuity to the projection using two ELMs. This new methodology shows considerable improvement over SOM on real datasets. Second, as a Supervised Learning method, ELMs has been applied to the hierarchical multiscale method to bridge the the molecular dynamics to continua. The method is tested on simulation data and proven to be efficient for passing the information from one scale to another. Lastly, the regularization of ELMs has been studied and a new regularization algorithm for ELMs is created using a modified Lanczos Algorithm. The Lanczos ELM on average divide computational time by 20 and reduce the Normalized MSE by 14% comparing with regular ELMs.
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Pehrson, Jakob, and Sara Lindstrand. "Support Unit Classification through Supervised Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281537.
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The purpose of this article is to evaluate the impact a supervised machine learning classification model can have on the process of internal customer support within a large digitized company. Chatbots are becoming a frequently used utility among digital services, though the true general impact is not always clear. The research is separated into the following two questions: (1) Which supervised machine learning algorithm of naïve Bayes, logistic regression, and neural networks can best predict the correct support a user needs and with what accuracy? And (2) What is the effect on the productivity and customer satisfaction of using machine learning to sort customer needs? The data was collected from the internal server database of a large digital company and was then trained on and tested with the three classification algorithms. Furthermore, a survey was collected with questions focused on understanding how the current system affects the involved employees. A first finding indicates that neural networks is the best suited model for the classification task. Though, when the scope and complexity was limited, naïve Bayes and logistic regression performed sufficiently. A second finding of the study is that the classification model potentially improves productivity given that the baseline is met. However, a difficulty exists in drawing conclusions on the exact effects on customer satisfaction since there are many aspects to take into account. Nevertheless, there is a good potential to achieve a positive net effect.Syftet med artikeln är att utvärdera den påverkan som en klassificeringsmodell kan ha på den interna processen av kundtjänst inom ett stort digitaliserat företag. Chatbotar används allt mer frekvent bland digitala tjänster, även om den generella effekten inte alltid är tydlig. Studien är uppdelad i följande två frågeställningar: (1) Vilken klassificeringsalgoritm bland naive Bayes, logistisk regression, och neurala nätverk kan bäst förutspå den korrekta hjälpen en användare är i behov av och med vilken noggrannhet? Och (2) Vad är effekten på produktivitet och kundnöjdhet för användandet av maskininlärning för sortering av kundbehov? Data samlades från ett stort, digitalt företags interna databas och används sedan i träning och testning med de tre klassificeringsalgoritmerna. Vidare, en enkät skickades ut med fokus på att förstå hur det nuvarande systemet påverkar de berörda arbetarna. Ett första fynd indikerar att neurala nätverk är den mest lämpade modellen för klassificeringen. Däremot, när omfånget och komplexiteten var begränsat presenterade även naive Bayes och logistisk regression tillräckligt. Ett andra fynd av studien är att klassificeringen potentiellt förbättrar produktiviteten givet att baslinjen är mött. Däremot existerar en svårighet i att dra slutsatser om den exakta effekten på kundnöjdhet eftersom det finns många olika aspekter att ta hänsyn till. Likväl finns en god potential i att uppnå en positiv nettoeffekt.
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Ramesh, Rohit. "Abnormality detection with deep learning." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/118542/1/Rohit_Ramesh_Thesis.pdf.
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This thesis is a step forward in developing the scientific basis for abnormality detection of individuals in crowded environments by utilizing a deep learning method. Such applications for monitoring human behavior in crowds is useful for public safety and security purposes.
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Faucheux, Cyrille. "Segmentation supervisée d'images texturées par régularisation de graphes." Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4050/document.
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Dans cette thèse, nous nous intéressons à un récent algorithme de segmentation d’images basé sur un processus de régularisation de graphes. L’objectif d’un tel algorithme est de calculer une fonction indicatrice de la segmentation qui satisfait un critère de régularité ainsi qu’un critère d’attache aux données. La particularité de cette approche est de représenter les images à l’aide de graphes de similarité. Ceux-ci permettent d’établir des relations entre des pixels non-adjacents, et ainsi de procéder à un traitement non-local des images. Afin d’en améliorer la précision, nous combinons cet algorithme à une seconde approche non-locale : des caractéristiques de textures. Un nouveau terme d’attache aux données est dans un premier temps développé. Inspiré des travaux de Chan et Vese, celui-ci permet d’évaluer l’homogénéité d’un ensemble de caractéristiques de textures. Dans un second temps, nous déléguons le calcul de l’attache aux données à un classificateur supervisé. Entrainé à reconnaitre certaines classes de textures, ce classificateur permet d’identifier les caractéristiques les plus pertinentes, et ainsi de fournir une modélisation plus aboutie du problème. Cette seconde approche permet par ailleurs une segmentation multiclasse. Ces deux méthodes ont été appliquées à la segmentation d’images texturées 2D et 3DIn this thesis, we improve a recent image segmentation algorithm based on a graph regularization process. The goal of this method is to compute an indicator function that satisfies a regularity and a fidelity criteria. Its particularity is to represent images with similarity graphs. This data structure allows relations to be established between similar pixels, leading to non-local processing of the data. In order to improve this approach, combine it with another non-local one: the texture features. Two solutions are developped, both based on Haralick features. In the first one, we propose a new fidelity term which is based on the work of Chan and Vese and is able to evaluate the homogeneity of texture features. In the second method, we propose to replace the fidelity criteria by the output of a supervised classifier. Trained to recognize several textures, the classifier is able to produce a better modelization of the problem by identifying the most relevant texture features. This method is also extended to multiclass segmentation problems. Both are applied to 2D and 3D textured images
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Aylas, Victor David Sanchez. "Contributions to Supervised Learning of Real-Valued Functions Using Neural Networks." NSUWorks, 1998. http://nsuworks.nova.edu/gscis_etd/395.
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This dissertation presents a new strategy for the automatic design of neural networks. The learning environment addressed is supervised learning from examples. Specifically, Radial Basis Functions (RBF) networks learning real-valued functions of real vectors as in non-linear regression applications are considered. The strategy is based upon the application of strong theoretical relationships between RBF networks and methods from approximation theory, robust statistics, and computational learning theory.
The complexity of the network design is examined in detail from the formal definition of the learning problem to the establishment of the corresponding optimization problem. A novel strategy for the systematic and automatic design of RBF networks is developed based upon the coordinated evaluation of memorization and generalization of an incremental architecture.
The architecture grows according to the monotonous increase of its generalization. Its corresponding learning method stands out due to its fast convergence and robustness. It represents one of the few learning methods whose computational complexity is precisely stated. It can be used in any non-linear regression tasks which are common in different disciplines of the natural and engineering sciences.
Four learning methods are implemented for evaluation. The most complex is the one for the novel self-generating network architecture. Another learning method constitutes a strong contribution to the area of robust learning allowing the automatic detection of data outliers and the removal of their negative influence in the network approximation. It represents the first robust learning method for RBF networks available in the literature and is integrated into the overall strategy introduced in this work. Diverse functions are used to simulate training and test data. Data generated for evaluation is: noise-free, noisy, and with outliers as well as one- and multidimensional.
The data with outliers allows the verification of the robustness of the introduced method. In addition, an evaluation example from the area of sensory data processing is chosen. This example consists in localizing a generic object based on range information in the framework of a grasping strategy. The relation to other works and a perspective for further research concludes this work.
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Lantz, Robin. "Time series monitoring and prediction of data deviations in a manufacturing industry." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-100181.
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An automated manufacturing industry makes use of many interacting moving parts and sensors. Data from these sensors generate complex multidimensional data in the production environment. This data is difficult to interpret and also difficult to find patterns in. This project provides tools to get a deeper understanding of Swedsafe’s production data, a company involved in an automated manufacturing business. The project is based on and will show the potential of the multidimensional production data. The project mainly consists of predicting deviations from predefined threshold values in Swedsafe’s production data. Machine learning is a good method of finding relationships in complex datasets. Supervised machine learning classification is used to predict deviation from threshold values in the data. An investigation is conducted to identify the classifier that performs best on Swedsafe's production data. The technique sliding window is used for managing time series data, which is used in this project. Apart from predicting deviations, this project also includes an implementation of live graphs to easily get an overview of the production data. A steady production with stable process values is important. So being able to monitor and predict events in the production environment can provide the same benefit for other manufacturing companies and is therefore suitable not only for Swedsafe. The best performing machine learning classifier tested in this project was the Random Forest classifier. The Multilayer Perceptron did not perform well on Swedsafe’s data, but further investigation in recurrent neural networks using LSTM neurons would be recommended. During the projekt a web based application displaying the sensor data in live graphs is also developed.
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Tatsumi, Keiji. "Studies on supervised learning for neural networks with applications to optimization problems." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/136029.
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