Dissertations / Theses on the topic 'Incremental neural network'
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Lundberg, Emil. "Adding temporal plasticity to a self-organizing incremental neural network using temporal activity diffusion." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180346.
Full textVektorkvantisering (VQ; eng: Vector Quantization) är ett klassiskt problem och en enkel metod för mönsterigenkänning. Bland tillämpningar finns förstörande datakompression, klustring och igenkänning av tal och talare. Även om VQ i stort har ersatts av tidsmedvetna tekniker såsom dolda Markovmodeller (HMM, eng: Hidden Markov Models) och dynamisk tidskrökning (DTW, eng: Dynamic Time Warping) i vissa tillämpningar, som tal- och talarigenkänning, har VQ ännu viss relevans tack vare sin mycket lägre beräkningsmässiga kostnad — särskilt för exempelvis inbyggda system. En ny studie demonstrerar också ett VQ-system med flera sektioner som åstadkommer prestanda i klass med DTW i en tillämpning på igenkänning av handskrivna signaturer, men till en mycket lägre beräkningsmässig kostnad. Att dra nytta av temporala mönster i en VQ-algoritm skulle kunna hjälpa till att förbättra sådana resultat ytterligare. SOTPAR2 är en sådan utökning av Neural Gas, en artificiell neural nätverk-algorithm för VQ. SOTPAR2 använder en konceptuellt enkel idé, baserad på att lägga till sidleds anslutningar mellan nätverksnoder och skapa “temporal aktivitet” som diffunderar genom anslutna noder. Aktiviteten gör sedan så att närmaste-granne-klassificeraren föredrar noder med hög aktivitet, och författarna till SOTPAR2 rapporterar förbättrade resultat jämfört med Neural Gas i en tillämpning på förutsägning av en tidsserie. I denna rapport undersöks hur samma utökning påverkar kvantiserings- och förutsägningsprestanda hos algoritmen självorganiserande inkrementellt neuralt nätverk (SOINN, eng: self-organizing incremental neural network). SOINN är en VQ-algorithm som automatiskt väljer en lämplig kodboksstorlek och också kan användas för klustring med godtyckliga klusterformer. Experimentella resultat visar att denna utökning inte förbättrar prestandan hos SOINN, istället försämrades prestandan i alla experiment som genomfördes. Detta resultat diskuteras, liksom inverkan av parametervärden på prestandan, och möjligt framtida arbete för att förbättra resultaten föreslås.
Flores, João Henrique Ferreira. "ARMA-CIGMN : an Incremental Gaussian Mixture Network for time series analysis and forecasting." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/116126.
Full textThis work presents a new model of neural network for time series analysis and forecasting: the ARMA-CIGMN (Autoregressive Moving Average Classical Incremental Gaussian Mixture Network) model and its analysis. This model is based on modifications made to a reformulated IGMN, the Classical IGMN (CIGMN). The CIGMN is similar to the original IGMN, but based on a classical statistical approach. The modifications to the IGMN algorithm were made to better fit it to time series. The proposed ARMA-CIGMN model demonstrates good forecasts and the modeling procedure can also be aided by known statistical tools as the autocorrelation (acf) and partial autocorrelation functions (pacf), already used in classical statistical time series modeling and also with the original IGMN algorithm models. The ARMA-CIGMN model was evaluated using known series and simulated data. The models used for comparisons were the classical statistical ARIMA model and its variants, the original IGMN and two modifications over the original IGMN: (i) a modification similar to a classical ARMA (Autoregressive Moving Average) model and (ii) a similar NOE (Nonlinear Output Error) model. It is also presented a reformulated IGMN version with a classical statistical approach, which is needed for the ARMA-CIGMN model.
Hocquet, Guillaume. "Class Incremental Continual Learning in Deep Neural Networks." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPAST070.
Full textWe are interested in the problem of continual learning of artificial neural networks in the case where the data are available for only one class at a time. To address the problem of catastrophic forgetting that restrain the learning performances in these conditions, we propose an approach based on the representation of the data of a class by a normal distribution. The transformations associated with these representations are performed using invertible neural networks, which can be trained with the data of a single class. Each class is assigned a network that will model its features. In this setting, predicting the class of a sample corresponds to identifying the network that best fit the sample. The advantage of such an approach is that once a network is trained, it is no longer necessary to update it later, as each network is independent of the others. It is this particularly advantageous property that sets our method apart from previous work in this area. We support our demonstration with experiments performed on various datasets and show that our approach performs favorably compared to the state of the art. Subsequently, we propose to optimize our approach by reducing its impact on memory by factoring the network parameters. It is then possible to significantly reduce the storage cost of these networks with a limited performance loss. Finally, we also study strategies to produce efficient feature extractor models for continual learning and we show their relevance compared to the networks traditionally used for continual learning
Ronco, Eric. "Incremental polynomial controller networks two self-organising non-linear controllers /." Thesis, Connect to electronic version, 1997. http://hdl.handle.net/1905/181.
Full textButtar, 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.
Full textMonica, Riccardo. "Deep Incremental Learning for Object Recognition." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12331/.
Full textPinto, Rafael Coimbra. "Continuous reinforcement learning with incremental Gaussian mixture models." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/157591.
Full textThis thesis’ original contribution is a novel algorithm which integrates a data-efficient function approximator with reinforcement learning in continuous state spaces. The complete research includes the development of a scalable online and incremental algorithm capable of learning from a single pass through data. This algorithm, called Fast Incremental Gaussian Mixture Network (FIGMN), was employed as a sample-efficient function approximator for the state space of continuous reinforcement learning tasks, which, combined with linear Q-learning, results in competitive performance. Then, this same function approximator was employed to model the joint state and Q-values space, all in a single FIGMN, resulting in a concise and data-efficient algorithm, i.e., a reinforcement learning algorithm that learns from very few interactions with the environment. A single episode is enough to learn the investigated tasks in most trials. Results are analysed in order to explain the properties of the obtained algorithm, and it is observed that the use of the FIGMN function approximator brings some important advantages to reinforcement learning in relation to conventional neural networks.
Pinto, Rafael Coimbra. "Online incremental one-shot learning of temporal sequences." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/49063.
Full textThis work introduces novel neural networks algorithms for online spatio-temporal pattern processing by extending the Incremental Gaussian Mixture Network (IGMN). The IGMN algorithm is an online incremental neural network that learns from a single scan through data by means of an incremental version of the Expectation-Maximization (EM) algorithm combined with locally weighted regression (LWR). Four different approaches are used to give temporal processing capabilities to the IGMN algorithm: time-delay lines (Time-Delay IGMN), a reservoir layer (Echo-State IGMN), exponential moving average of reconstructed input vector (Merge IGMN) and self-referencing (Recursive IGMN). This results in algorithms that are online, incremental, aggressive and have temporal capabilities, and therefore are suitable for tasks with memory or unknown internal states, characterized by continuous non-stopping data-flows, and that require life-long learning while operating and giving predictions without separated stages. The proposed algorithms are compared to other spatio-temporal neural networks in 8 time-series prediction tasks. Two of them show satisfactory performances, generally improving upon existing approaches. A general enhancement for the IGMN algorithm is also described, eliminating one of the algorithm’s manually tunable parameters and giving better results.
Glöde, Isabella. "Autonomous control of a mobile robot with incremental deep learning neural networks." Master's thesis, Pontificia Universidad Católica del Perú, 2021. http://hdl.handle.net/20.500.12404/18676.
Full textThuv, Øyvin Halfdan. "Incrementally Evolving a Dynamic Neural Network for Tactile-Olfactory Insect Navigation." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8834.
Full textThis Masters thesis gives a thorough description of a study carried out in the Self-Organizing Systems group at the NTNU. Much {AI research in the later years has moved towards increased use of representationless strategies such as simulated neural networks. One technique for creating such networks is to evolve them using simulated Darwinian evolution. This is a powerful technique, but it is often limited by the computer resources available. One way to speed up evolution, is to focus the evolutionary search on a more narrow range of solutions. It is for example possible to favor evolution of a specific ``species'' by initializing the search with a specialized set of genes. A disadvantage of doing this is of course that many other solutions (or ``species'') are disregarded so that good solutions in theory may be lost. It is therefore necessary to find focusing strategies that are generally applicable and (with a high probability) only disregards solutions that are considered unimportant. Three different ways of focusing evolutionary search for cognitive behaviours are merged and evaluated in this thesis: On a macro level, incremental evolution is applied to partition the evolutionary search. On a micro level, specific properties of the chosen neural network model (CTRNNs) are exploited. The two properties are seeding initial populations with center-crossing neural networks and/or bifurcative neurons. The techniques are compared to standard, naive, evolutionary searches by applying them to the evolution of simulated neural networks for the walking and control of a six-legged mobile robot. A problem simple enough to be satisfactorily understood, but complex enough to be a challenge for a traditional evolutionary search.
Johansson, Philip. "Incremental Learning of Deep Convolutional Neural Networks for Tumour Classification in Pathology Images." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158225.
Full textSoares, Sérgio Aurélio Ferreira. "Spatial interpolation and geostatistic simulation with the incremental Gaussian mixture network." reponame:Repositório Institucional da UFSC, 2016. https://repositorio.ufsc.br/xmlui/handle/123456789/178581.
Full textMade available in DSpace on 2017-08-22T04:22:16Z (GMT). No. of bitstreams: 1 347911.pdf: 1690914 bytes, checksum: e43f9150ef3cb130f6d5696b46a68fa5 (MD5) Previous issue date: 2016
Abstract : Geostatistics aggregates a set of tools designed to deal with spatially correlated data. Two significant problems that Geostatistics tackles are the spatial interpolation and geostatistical simulation. Kriging and Sequential Gaussian Simulation (SGS) are two examples of traditional geostatistical tools used for these kinds of problems. These methods perform well when the provided Variogram is well modeled. The problem is that modeling the Variogram requires expert knowledge and a certain familiarity with the dataset. This complexity might make Geostatistics tools the last choice of a non-expert. On the other hand, an important feature present in neural networks is their ability to learn from data, even when the user does not have much information about the particular dataset. However, traditional models, such as Multilayer Perceptron (MLP), do not perform well in spatial interpolation problems due to their difficulty in accurately modeling the spatial correlation between samples. With this motivation in mind, we adapted the Incremental Gaussian Mixture Network (IGMN) model for spatial interpolation and geostatistical simulation applications. The three most important contributions of this work are: 1. An improvement in the IGMN estimation process for spatial interpolation problems with sparse datasets; 2. An algorithm to perform Sequential Gaussian Simulation using IGMN instead of Kriging; 3. An algorithm that mixes the Direct Sampling (DS) method and IGMN for cluster-based Multiple Point Simulation (MPS) with training images. Results show that our approach outperforms MLP and the original IGMN in spatial interpolation problems, especially in anisotropic and sparse datasets (in terms of RMSE and CC). Also, our algorithm for sequential simulation using IGMN instead of Kriging can generate equally probable realizations of the defined simulation grid for unconditioned simulations. Finally, our algorithm that mixes the DS method and IGMN can produce better quality simulations and runs much faster than the original DS. To the best of our knowledge, this is the first time a Neural Network model is specialized for spatial interpolation applications and can perform a geostatistical simulation.
A Geoestatística agrega um conjunto de ferramentas especializadas em dados espacialmente correlacionados. Dois problemas importantes na Geoestatística são a interpolação espacial e a simulação. A Krigagem e a Simulação Sequencial Gaussiana (SGS) são dois exemplos de ferramentas geoestatísticas utilizadas para esses tipos de problemas, respectivamente. A Krigagem e a SGS possuem bom desempenho quando o Variograma fornecido pelo usuário representa bem as correlações espaciais. O problema é que a modelagem do Variograma requer um conhecimento especializado e certa familiaridade com o conjunto de dados em estudo. Essa complexidade pode tornar difícíl a popularização dessas técnicas entre não-especialistas. Por outro lado, uma característica importante presente em Redes Neurais Artificiais é a capacidade de aprender a partir dos dados, mesmo quando o usuário não possui familiaridade com os dados. No entanto, os modelos tradicionais, como o Multilayer Perceptron (MLP), têm dificuldade em identificar a correlação espacial entre amostras e não apresentam um bom desempenho em problemas de interpolação espacial. Com essa motivação, nós adaptamos e aplicamos a Incremental Gaussian Mixture Network (IGMN) em problemas de interpolação espacial e simulação geoestatística. As três principais contribuições deste trabalho são: 1. Melhoria no processo de estimação da IGMN para problemas de interpolação espacial; 2. Um algoritmo para realizar simulação sequencial gaussiana utilizando a IGMN como interpolador; 3. Um algoritmo que mistura o método Direct Sampling (DS) e a IGMN para realizar simulação multiponto (MPS) a partir de imagens de treinamento. Os resultados mostram que a nossa abordagem é mais precisa que o MLP e a IGMN original em problemas de interpolação espacial, especialmente em conjuntos de dados esparsos e com anisotropia (em termos de RMSE e CC). Nosso algoritmo de simulação sequencial que utiliza a IGMN como interpolador é capaz de gerar simulações não condicionadas que respeitam características do conjunto original de dados. Finalmente, nosso algoritmo de simulação multiponto, que mistura o método DS e a IGMN, é capaz de realizar simulações condicionadas e produz realizações com qualidade superior num tempo de execução inferior ao do DS. Até onde sabemos, esta a primeira vez que um modelo de rede neural é especializado para aplicações de interpolação espacial e é capaz de realizar simulação geostatística.
Heinen, Milton Roberto. "A connectionist approach for incremental function approximation and on-line tasks." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/29015.
Full textThis work proposes IGMN (standing for Incremental Gaussian Mixture Network), a new connectionist approach for incremental function approximation and real time tasks. It is inspired on recent theories about the brain, specially the Memory-Prediction Framework and the Constructivist Artificial Intelligence, which endows it with some unique features that are not present in most ANN models such as MLP, RBF and GRNN. Moreover, IGMN is based on strong statistical principles (Gaussian mixture models) and asymptotically converges to the optimal regression surface as more training data arrive. The main advantages of IGMN over other ANN models are: (i) IGMN learns incrementally using a single scan over the training data (each training pattern can be immediately used and discarded); (ii) it can produce reasonable estimates based on few training data; (iii) the learning process can proceed perpetually as new training data arrive (there is no separate phases for leaning and recalling); (iv) IGMN can handle the stability-plasticity dilemma and does not suffer from catastrophic interference; (v) the neural network topology is defined automatically and incrementally (new units added whenever is necessary); (vi) IGMN is not sensible to initialization conditions (in fact there is no random initialization/ decision in IGMN); (vii) the same neural network can be used to solve both forward and inverse problems (the information flow is bidirectional) even in regions where the target data are multi-valued; and (viii) IGMN can provide the confidence levels of its estimates. Another relevant contribution of this thesis is the use of IGMN in some important state-of-the-art machine learning and robotic tasks such as model identification, incremental concept formation, reinforcement learning, robotic mapping and time series prediction. In fact, the efficiency of IGMN and its representational power expand the set of potential tasks in which the neural networks can be applied, thus opening new research directions in which important contributions can be made. Through several experiments using the proposed model it is demonstrated that IGMN is also robust to overfitting, does not require fine-tunning of its configuration parameters and has a very good computational performance, thus allowing its use in real time control applications. Therefore, IGMN is a very useful machine learning tool for incremental function approximation and on-line prediction.
Pereira, Renato de Pontes. "HIGMN : an IGMN-based hierarchical architecture and its applications for robotic tasks." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/80752.
Full textThe recent field of Deep Learning has introduced to Machine Learning new meth- ods based on distributed abstract representations of the training data throughout hierarchical structures. The hierarchical organization of layers allows these meth- ods to store distributed information on sensory signals and to create concepts with different abstraction levels to represent the input data. This work investigates the impact of a hierarchical structure inspired by ideas on Deep Learning and based on the Incremental Gaussian Mixture Network (IGMN), a probabilistic neural network with an on-line and incremental learning, specially suitable for robotic tasks. As a result, a hierarchical architecture, called Hierarchical Incremental Gaussian Mixture Network (HIGMN), was developed, which combines two levels of IGMNs. The HIGMN first-level layers are able to learn concepts from data of different domains that are then related in the second-level layer. The proposed model was compared with the IGMN regarding robotic tasks, in special, the task of learning and repro- ducing a wall-following behavior, based on a Learning from Demonstration (LfD) approach. The experiments showed how the HIGMN can perform parallely three different tasks concept learning, behavior segmentation, and learning and repro- ducing behaviors and its ability to learn a wall-following behavior and to perform it in unknown environments with new sensory information. HIGMN could reproduce the wall-following behavior after a single, simple, and short demonstration of the behavior. Moreover, it acquired different types of knowledge: information on the environment, the robot kinematics, and the target behavior.
Cholet, Stéphane. "Evaluation automatique des états émotionnels et dépressifs : vers un système de prévention des risques psychosociaux." Thesis, Antilles, 2019. http://www.theses.fr/2019ANTI0388/document.
Full textPsychosocial risks are a major public health issue, because of the disorders they can trigger : stress, mood swings, burn-outs, etc. Although propoer diagnosis can only be made by a healthcare professionnel, Affective Computing can make a contribution by improving the understanding of the phenomena. Affective Computing is a multidisciplinary field involving concepts of Artificial Intelligence, psychology and psychiatry, among others. In this research, we are interested in two elements that can be subject to disorders: the emotional state and the depressive state of individuals.The concept of emotion covers a wide range of definitions and models, most of which are based on work in psychiatry or psychology. A famous example is Russell's circumplex, which defines an emotion as the combination of two emotional dimensions, called valence and arousal. Valence denotes an individual's sad or joyful character, while arousal denotes his passive or active character. The automatic evaluation of emotional states has generated a significant revival of interest in the last decade. Methods from Artificial Intelligence allow to achieve interesting performances, from data captured in a non-invasive manner, such as videos. However, there is one aspect that has not been studied much: that of emotional intensities and the possibility of recognizing them. In this thesis, we have explored this aspect using visualization and classification methods to show that the use of emotional intensity classes, rather than continuous values, benefits both automatic recognition and state interpretation.The concept of depression is more strict, as it is a recognized disease as such. It affects individuals regardless of age, gender or occupation, but varies in intensity or nature of symptoms. For this reason, its study, both at the level of detection and monitoring, is of major interest for the prevention of psychosocial risks.However, his diagnosis is made difficult by the sometimes innocuous nature of the symptoms and by the often delicate process of consulting a specialist. The Beck's scale and the associated score allow, by means of a questionnaire, to evaluate the severity of an individual's state of depression. The system we have developed is able to automatically recognize an individual's depressive score from videos. It includes, on the one hand, a low-level visual spatio-temporal descriptor that quantifies micro and macro facial movements and, on the other hand, neural methods from the cognitive sciences. Its speed allows applications for real-time recognition of depressive states, and its performance is interesting with regard to the state of the art. The fusion of visual and auditory modalities has also been studied, showing that the use of these two sensory channels benefits the recognition of depressive states.Beyond performance and originality, one of the strong points of this thesis is the interpretability of the methods. Indeed, in a multidisciplinary context such as that of Affective Computing, improving knowledge and understanding of the studied phenomena is a key point that usual computer methods implemeted as "black boxes" can't deal with
Börthas, Lovisa, and Sjölander Jessica Krange. "Machine Learning Based Prediction and Classification for Uplift Modeling." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266379.
Full textBehovet av att kunna modellera den verkliga vinsten av riktad marknadsföring har lett till den idag vanligt förekommande metoden inkrementell responsanalys. För att kunna utföra denna typ av metod krävs förekomsten av en existerande testgrupp samt kontrollgrupp och målet är således att beräkna differensen mellan de positiva utfallen i de två grupperna. Sannolikheten för de positiva utfallen för de två grupperna kan effektivt estimeras med statistiska maskininlärningsmetoder. De inkrementella responsanalysmetoderna som undersöks i detta projekt är subtraktion av två modeller, att modellera den inkrementella responsen direkt samt en klassvariabeltransformation. De statistiska maskininlärningsmetoderna som tillämpas är random forests och neurala nätverk samt standardmetoden logistisk regression. Datan är samlad från ett väletablerat detaljhandelsföretag och målet är därmed att undersöka vilken inkrementell responsanalysmetod och maskininlärningsmetod som presterar bäst givet datan i detta projekt. De mest avgörande aspekterna för att få ett bra resultat visade sig vara variabelselektionen och mängden kontrolldata i varje dataset. För att få ett lyckat resultat bör valet av maskininlärningsmetod vara random forests vilken används för att modellera den inkrementella responsen direkt, eller logistisk regression tillsammans med en klassvariabeltransformation. Neurala nätverksmetoder är känsliga för ojämna klassfördelningar och klarar därmed inte av att erhålla stabila modeller med den givna datan. Vidare presterade subtraktion av två modeller dåligt på grund av att var modell tenderade att fokusera för mycket på att modellera klassen i båda dataseten separat, istället för att modellera differensen mellan dem. Slutsatsen är således att en metod som modellerar den inkrementella responsen direkt samt en relativt stor kontrollgrupp är att föredra för att få ett stabilt resultat.
Hamid, Muhammed Hamed. "Hyperspectral Image Generation, Processing and Analysis." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5905.
Full textOsório, Fernando Santos. "Inss : un système hybride neuro-symbolique pour l'apprentissage automatique constructif." Grenoble INPG, 1998. https://tel.archives-ouvertes.fr/tel-00004899.
Full textVarious Artificial Intelligence methods have been developed to reproduce intelligent human behaviour. These methods allow to reproduce some human reasoning process using the available knowledge. Each method has its advantages, but also some drawbacks. Hybrid systems combine different approaches in order to take advantage of their respective strengths. These hybrid intelligent systems also present the ability to acquire new knowledge from different sources and so to improve their application performance. This thesis presents our research in the field of hybrid neuro-symbolic systems, and in particular the study of machine learning tools used for constructive knowledge acquisition. We are interested in the automatic acquisition of theoretical knowledge (rules) and empirical knowledge (examples). We present a new hybrid system we implemented: INSS - Incremental Neuro-Symbolic System. This system allows knowledge transfer from the symbolic module to the connectionist module (Artificial Neural Network - ANN), through symbolic rule compilation into an ANN. We can refine the initial ANN knowledge through neural learning using a set of examples. The incremental ANN learning method used, the Cascade-Correlation algorithm, allows us to change or to add new knowledge to the network. Then, the system can also extract modified (or new) symbolic rules from the ANN and validate them. INSS is a hybrid machine learning system that implements a constructive knowledge acquisition method. We conclude by showing the results we obtained with this system in different application domains: ANN artificial problems(The Monk's Problems), computer aided medical diagnosis (Toxic Comas), a cognitive modelling task (The Balance Scale Problem) and autonomous robot control. The results we obtained show the improved performance of INSS and its advantages over others hybrid neuro-symbolic systems
Besedin, Andrey. "Continual forgetting-free deep learning from high-dimensional data streams." Electronic Thesis or Diss., Paris, CNAM, 2019. http://www.theses.fr/2019CNAM1263.
Full textIn this thesis, we propose a new deep-learning-based approach for online classification on streams of high-dimensional data. In recent years, Neural Networks (NN) have become the primary building block of state-of-the-art methods in various machine learning problems. Most of these methods, however, are designed to solve the static learning problem, when all data are available at once at training time. Performing Online Deep Learning is exceptionally challenging.The main difficulty is that NN-based classifiers usually rely on the assumption that the sequence of data batches used during training is stationary, or in other words, that the distribution of data classes is the same for all batches (i.i.d. assumption).When this assumption does not hold Neural Networks tend to forget the concepts that are temporarily not available in thestream. In the literature, this phenomenon is known as catastrophic forgetting. The approaches we propose in this thesis aim to guarantee the i.i.d. nature of each batch that comes from the stream and compensates for the lack of historical data. To do this, we train generative models and pseudo-generative models capable of producing synthetic samples from classes that are absent or misrepresented in the stream and complete the stream’s batches with these samples. We test our approaches in an incremental learning scenario and a specific type of continuous learning. Our approaches perform classification on dynamic data streams with the accuracy close to the results obtained in the static classification configuration where all data are available for the duration of the learning. Besides, we demonstrate the ability of our methods to adapt to invisible data classes and new instances of already known data categories, while avoiding forgetting the previously acquired knowledge
Jiech-Chyn, Wu, and 吳戒秦. "Surface Data Compression by Incremental Approximation of RBF Neural Network." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/85819174739885685144.
Full text國立臺灣大學
電機工程研究所
84
The measurement of three-dimension surface by scanning usually generates huge amount of data occupying very large space of storage. In stead of saving the dimensional data directly, this paper proposes to approximate the surface by learning the measured data with a neural network and then simply saves the weights of the neural network in the storage. This approach is found to be able to achieve simultaneously the results of approximation, smoothing and, more importantly, compression of the surface data. The Radial Basis Function (RBF) Neura Network is chosen to be the approximator due to its performance in accuracy. The learning algorithm is developed on the basis of Recursive Least Square Method (RLSM) so that incremental learning of the surface can be achieved. The capability of incremental learning allows the approximation by the Neural Network to be improved along with the addition of new measurements of the surface. Since the accuracy of approximating a surface by the Neural Network with fixed numbers of layers and nodes is closely related to the complexity or order of the target surface. An algorithm of adaptive segmentation is developed to split the surface data into segments each with suitable complexity for approximation. Given the desired accuracy of approximation, it is shown that the RBF Neural Network based approximator can obtain large ratio of data compression. Several numerical examples are demonstrated.
Іванова, Є. В. "Самоорганізовна інкрементна нейронна мережа для кластерування масивів даних." Thesis, 2018. https://openarchive.nure.ua/handle/document/20022.
Full textVan, der Stockt Stefan Aloysius Gert. "A generic neural network framework using design patterns." Diss., 2008. http://hdl.handle.net/2263/27614.
Full textDissertation (MSc)--University of Pretoria, 2008.
Computer Science
unrestricted
De, Wet Anton Petrus Christiaan. "An incremental learning system for artificial neural networks." Thesis, 2014. http://hdl.handle.net/10210/12024.
Full textThis dissertation describes the development of a system of Artificial Neural Networks that enables the incremental training of feed forward neural networks using supervised training algorithms such as back propagation. It is argued that incremental learning is fundamental to the adaptive learning behavior observed in human intelligence and constitutes an imperative step towards artificial cognition. The importance of developing incremental learning as a system of ANNs is stressed before the complete system is presented. Details of the development and implementation of the system is complemented by the description of two case studies. In conclusion the role of the incremental learning system as basis for further development of fundamental elements of cognition is projected.
Wu, Yi-Lian, and 吳易璉. "Integrated the Validation Incremental Neural Networks and Radial-Basis Function Neural Networks for Segmenting Prostate." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/92540595174319775086.
Full text國立雲林科技大學
資訊工程研究所
97
Recently, Transrectal ultrasoundgraphy (TRUS) imaging is widely used to diagnose prostate disease. Before a physician can diagnose prostate lesions, contour of the prostate in TRUS images must be manually outlined. However, manual segmentation is time-consuming and inefficient. Therefore, an automatic segmentation of prostate in TRUS images is necessary. Among the segmentation methods, active contour model (ACM) is a successful contour detection method. But the shortcoming of ACM is that the determination of the initial contour is manual. Thus, in this paper, an automatic neural-network-based prostate segmentation method in TRUS images is proposed, which can omit the complicated step of determine the initial contour. The proposed system consists of the Validation Incremental Neural Network and Radial-Basis Function Neural Networks for prostate segmentation. Experimental results show that the proposed method has higher accuracy than Active Contour Model (ACM).
Obenauff, Alexander. "A progressive learning method for classification of manufacturing errors based on machine data." Master's thesis, 2019. http://hdl.handle.net/10362/76579.
Full textManufacturing companies face significant market pressure in today’s globalised world. Fierce global competition and product individualisation mean that production systems require continuous optimisation. This means that automation, flexibility and efficiency have all become vital elements for manufacturers. In this paper, a method based on incremental classification used for manufacturing errors is presented. The analysis and classification focus on data of binary form collected from a machine control unit during manufacturing operation in real time. Various methods that can learn from data incrementally and autonomously are to be applied. The training starts with the least amount of data possible and other important steps like data preprocessing are reviewed under the aspect of incremental learning.
"Incremental Learning With Sample Generation From Pretrained Networks." Master's thesis, 2020. http://hdl.handle.net/2286/R.I.57207.
Full textDissertation/Thesis
Masters Thesis Computer Science 2020
Mohamed, Shakir. "Dynamic protein classification: Adaptive models based on incremental learning strategies." Thesis, 2008. http://hdl.handle.net/10539/4678.
Full textHUANG, HONG-WEI, and 黃弘偉. "Abnormal Moving Object Detection under Various Enviroments Using Self-Organizing Incremental Neural Networks." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/c54ss4.
Full text國立臺灣科技大學
資訊工程系
100
Abnormal moving objects detection is an essential issue for video surveillance. In order to judge whether the behavior of objects is abnormal, such as pedestrians walk back and forth, walk across the street, or scooters drive the wrong way, the main method is through computer vision technique to analyze objects as pedestrians, cars, and so on in video. Traditional abnormal moving objects detection aims at particular circumstances or requirement to predefine particular detection rules which the application of abnormal moving objects detection is restricted. Besides, if numerous abnormal moving objects are detected at the same time, surveillance system is overloaded with operation. Owing to this reason, in this paper, we expect to design a set of learning model which does not predefine abnormal rules and can detect a variety of abnormal moving objects automatically in different environments. To achieve the above goal, the first thing is to detect the moving objects in video. The proposed method in this paper utilizes Gaussian Mixture Model (GMM) to detect foreground objects and remove shadows of objects by shadow removal. Then, adoptive mean shift algorithm with Kalman filter is proposed to track these moving objects. Finally, Kalman filter is used to smooth trajectory. After collecting the trajectories of moving objects, abnormal moving object detection process proceeds. At first, for this trajectory information, take advantage of Self-Organizing Incremental Neural Network (SOINN) to learn and build a normal trajectory model which is a foundation to determine whether follow-up moving objects are abnormal. The average learning time is 7 to 55 seconds. The experiment monitors and analyzes different circumstances, such as School campus, roads, and one-way street. The system based on the proposed method can detect abnormal moving objects with the accuracy 100% in school campus, 98.3% in roads, and 98.8% in one-way street. The overall execution time is short and about 0.033 to 0.067 seconds, and it can be executed in real-time.
Chang, Da-Yuan, and 張大元. "A Study on Water Stage Increment of the River due to Reservoir Drainage by Artificial Neural Network." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/20190379759396040336.
Full text中原大學
土木工程研究所
92
Abstract In Taiwan, roughly 78% of its yearly rainfall concentrates in the summer and autumn because of the particular climate and geographic characteristics. During Typhoon period, the reservoir operators often face the dilemma of maintaining more floodwater and taking the risk of failure of the dam and taking the risk of being drought if excess floodwater is released. The most difficult task of reservoir operation is to consider all the functions of the reservoir. To achieve this purpose, forecasting the inflows of reservoir and simulating downstream water-stage due to the drainage of reservoir are essential to operators. Watershed of Shihmen reservoir and Da-han River are taken as demonstrations in this paper. The Artificial Neural Network (ANN) simulates the characteristics of river basin, and a “rainfall-runoff” model is established. While the model is built, we could predict the inflows of several hours later. In addition, the relationship between drainage and water-stage is found. Some important results and a three-dimension plot of drainage, water-stage and tide are present in this paper. The plot could figure out relationships between drainage and water-stage under different rainfall intensity or tide-level conditions. It is expected that this research be used for online reservoir operation in the future.
Santos, Veríssimo Manuel Brandão Lima. "Deteção automática de lesões no intestino delgado por análise de imagens obtidas por cápsula endoscópica." Doctoral thesis, 2020. http://hdl.handle.net/1822/76137.
Full textNas últimas duas décadas foram propostas numerosas metodologias de deteção automática de lesões por análise de imagens obtidas por cápsula endoscópica, com vista à automatização do moroso processo de revisão das imagens, utilizando uma grande variedade de pré-processamentos e classificadores. Um contributo significativo para aumentar a eficácia na deteção de lesões poderá ser obtido pelo uso de classificadores de elevado desempenho. Os conjuntos de dados obtidos em endoscopia por cápsula frequentemente apresentam distribuições multimodais, que tornam as fronteiras de classificação complexas e com características diversas. Por este motivo foi definida a hipótese de que a utilização de um classificador por ensemble decompondo o problema em subproblemas mais pequenos e simples conduziria porventura aos melhores resultados. Foram analisados os ensembles existentes, identificada a razão pela qual perdem desempenho em modo de aprendizagem incremental e proposta uma nova estrutura de ensemble adequada a funcionar neste modo de adaptação. Através de resultados experimentais foi verificada a eficiência do método proposto e observada a vantagem em utilizar aprendizagem incremental, embora neste último caso a limitação imposta pela base de dados utilizada, não tenha permitido obter diferenças mais significativas que no entanto se observam nas mais diversas áreas da engenharia como por exemplo no reconhecimento automático da fala.
Over the past two decades, numerous methodologies for automatic lesion detection by endoscopic capsule image analysis have been proposed to automate the cumbersome image reviewing process using a wide variety of preprocessing methodologies and classifiers. Increased effectiveness in lesion detection can be obtained using high-performance classifiers. Capsule endoscopy datasets often exhibit multimodal distributions with complex and diverse classification boundaries. For this reason, it was hypothesized that the use of an ensemble classifier to decompose the original problem into smaller and simpler subproblems would lead to better results. Current ensembles were analyzed, and the reason for the incremental mode performance loss identified. To overcome this limitation a new ensemble classifier more suited to this learning mode has been proposed. Experimental results showed the effectiveness of this new ensemble classifier and the benefits of incremental learning, although in the later the dataset limited the expected performance increase, that happens in many of engineering problems such as automatic speech recognition.