Rozprawy doktorskie na temat „Unsupervised neural networks”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Sprawdź 50 najlepszych rozpraw doktorskich naukowych na temat „Unsupervised neural networks”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Przeglądaj rozprawy doktorskie z różnych dziedzin i twórz odpowiednie bibliografie.
Nyamapfene, Abel. "Unsupervised multimodal neural networks". Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/844064/.
Pełny tekst źródłaMacdonald, Donald. "Unsupervised neural networks for visualisation of data". Thesis, University of the West of Scotland, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395687.
Pełny tekst źródłaBerry, Ian Michael. "Data classification using unsupervised artificial neural networks". Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390079.
Pełny tekst źródłaHarpur, George Francis. "Low entropy coding with unsupervised neural networks". Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627227.
Pełny tekst źródłaWalcott, Terry Hugh. "Market prediction for SMEs using unsupervised neural networks". Thesis, University of East London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.532991.
Pełny tekst źródłaVetcha, Sarat Babu. "Fault diagnosis in pumps by unsupervised neural networks". Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300604.
Pełny tekst źródłaBishop, Griffin R. "Unsupervised Semantic Segmentation through Cross-Instance Representation Similarity". Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-theses/1371.
Pełny tekst źródłaPlumbley, Mark David. "An information-theoretic approach to unsupervised connectionist models". Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387051.
Pełny tekst źródłaGaltier, Mathieu. "A mathematical approach to unsupervised learning in recurrent neural networks". Paris, ENMP, 2011. https://pastel.hal.science/pastel-00667368.
Pełny tekst źródłaIn this thesis, we propose to give a mathematical sense to the claim: the neocortex builds itself a model of its environment. We study the neocortex as a network of spiking neurons undergoing slow STDP learning. By considering that the number of neurons is close to infinity, we propose a new mean-field method to find the ''smoother'' equation describing the firing-rate of populations of these neurons. Then, we study the dynamics of this averaged system with learning. By assuming the modification of the synapses' strength is very slow compared the activity of the network, it is possible to use tools from temporal averaging theory. They lead to showing that the connectivity of the network always converges towards a single equilibrium point which can be computed explicitely. This connectivity gathers the knowledge of the network about the world. Finally, we analyze the equilibrium connectivity and compare it to the inputs. By seeing the inputs as the solution of a dynamical system, we are able to show that the connectivity embedded the entire information about this dynamical system. Indeed, we show that the symmetric part of the connectivity leads to finding the manifold over which the inputs dynamical system is defined, and that the anti-symmetric part of the connectivity corresponds to the vector field of the inputs dynamical system. In this context, the network acts as a predictor of the future events in its environment
Haddad, Josef, i Carl Piehl. "Unsupervised anomaly detection in time series with recurrent neural networks". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259655.
Pełny tekst źródłaArtificiella neurala nätverk (ANN) har tillämpats på många problem. Däremot försöker inte de flesta ANN-modeller efterlikna hjärnan i detalj. Ett exempel på ett ANN som är begränsat till att efterlikna hjärnan är Hierarchical Temporal Memory (HTM). Denna studie tillämpar HTM och Long Short-Term Memory (LSTM) på avvikelsedetektionsproblem i tidsserier för att undersöka vilka styrkor och svagheter de har för detta problem. Avvikelserna i denna studie är begränsade till punktavvikelser och tidsserierna är i endast en variabel. Redan existerande implementationer som utnyttjar dessa nätverk för oövervakad avvikelsedetektionsproblem i tidsserier används i denna studie. Vi använder främst våra egna syntetiska tidsserier för att undersöka hur nätverken hanterar brus och hur de hanterar olika egenskaper som en tidsserie kan ha. Våra resultat visar att båda nätverken kan hantera brus och prestationsskillnaden rörande brusrobusthet var inte tillräckligt stor för att urskilja modellerna. LSTM presterade bättre än HTM på att upptäcka punktavvikelser i våra syntetiska tidsserier som följer en sinuskurva men en slutsats angående vilket nätverk som presterar bäst överlag är fortfarande oavgjord.
Zeltner, Felix. "Autonomous Terrain Classification Through Unsupervised Learning". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60893.
Pełny tekst źródłaMohammed, Derek. "A Comparative Study of Unsupervised Neural Networks in Detecting Financial Misstatements". NSUWorks, 2005. http://nsuworks.nova.edu/gscis_etd/730.
Pełny tekst źródłaManne, Mihira. "MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS". Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3977.
Pełny tekst źródłaSuits, David B. "A simplified drive-reinforcement model for unsupervised learning in artificial neural networks /". Online version of thesis, 1992. http://hdl.handle.net/1850/11087.
Pełny tekst źródłaYang, Li. "Biologically inspired visual models by sparse and unsupervised learning : a dissertation /". Full text open access at:, 2007. http://content.ohsu.edu/u?/etd,163.
Pełny tekst źródłaGeigel, Arturo. "Unsupervised Learning Trojan". NSUWorks, 2014. http://nsuworks.nova.edu/gscis_etd/17.
Pełny tekst źródłaAbidogun, Olusola Adeniyi. "Data mining, fraud detection and mobile telecommunications: call pattern analysis with unsupervised neural networks". Thesis, University of the Western Cape, 2005. http://etd.uwc.ac.za/index.php?module=etd&.
Pełny tekst źródłamarketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing purpose are unlabeled. Further analysis is thus, required to be able to isolate fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process.
This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a period of time in a mobile telecommunication network. Our study provides a comparative analysis and application of Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) recurrent neural networks algorithms to user call data records in order to conduct a descriptive data mining on users call patterns.
Our investigation shows the learning ability of both techniques to discriminate user call patterns
the LSTM recurrent neural network algorithm providing a better discrimination than the SOM algorithm in terms of long time series modelling. LSTM discriminates different types of temporal sequences and groups them according to a variety of features. The ordered features can later be interpreted and labeled according to specific requirements of the mobile service provider. Thus, suspicious call behaviours are isolated within the mobile telecommunication network and can be used to to identify fraudulent call patterns. We give results using masked call data
from a real mobile telecommunication network.
Charles, Eugene Yougarajah Andrew. "Supervised and unsupervised weight and delay adaptation learning in temporal coding spiking neural networks". Thesis, Cardiff University, 2006. http://orca.cf.ac.uk/56168/.
Pełny tekst źródłaChavez, Wesley. "An Exploration of Linear Classifiers for Unsupervised Spiking Neural Networks with Event-Driven Data". PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4439.
Pełny tekst źródłaNikbakht, Silab Rasoul. "Unsupervised learning for parametric optimization in wireless networks". Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/671246.
Pełny tekst źródłaAqueta tesis estudia l’optimització paramètrica a les xarxes cel.lulars i xarxes cell-free, explotant els paradigmes basats en dades i basats en experts. L’assignació i control de la potencia, que ajusten la potencia de transmissió per complir amb diferents criteris d’equitat com max-min o max-product, son tasques crucials en les telecomunicacions inalàmbriques pertanyents a la categoria d’optimització paramètrica. Les tècniques d’última generació per al control i assignació de la potència solen exigir enormes costos computacionals i no son adequats per aplicacions en temps real. Per abordar aquesta qüestió, desenvolupem una tècnica de propòsit general utilitzant aprenentatge no supervisat per resoldre optimitzacions paramètriques; i al mateix temps ampliem el reconegut algoritme de control de potencia fraccionada. En el paradigma basat en dades, creem un marc d’aprenentatge no supervisat que defineix una xarxa neuronal (NN, sigles de Neural Network en Anglès) especifica, incorporant coneixements experts a la funció de cost de la NN per resoldre els problemes de control i assignació de potència. Dins d’aquest enfocament, s’entrena una NN de tipus feedforward mitjançant el mostreig repetit en l’espai de paràmetres, però, en lloc de resoldre completament el problema d’optimització associat, es pren un sol pas en la direcció del gradient de la funció objectiu. El mètode resultant ´es aplicable tant als problemes d’optimització convexos com no convexos. Això ofereix una acceleració de dos a tres ordres de magnitud en els problemes de control i assignació de potencia en comparació amb un algoritme de resolució convexa—sempre que sigui aplicable. En el paradigma dirigit per experts, investiguem l’extensió del control de potencia fraccionada a les xarxes sense cèl·lules. La solució tancada resultant pot ser avaluada per a l’enllaç de pujada i el de baixada sense esforç i assoleix una solució (gaire) òptima en el cas de l’enllaç de pujada. En ambdós paradigmes, ens centrem especialment en els guanys a gran escala—la quantitat d’atenuació que experimenta la potencia mitja local rebuda. La naturalesa de variació lenta dels guanys a gran escala relaxa la necessitat d’una actualització freqüent de les solucions tant en el paradigma basat en dades com en el basat en experts, permetent d’aquesta manera l’ús dels dos mètodes en aplicacions en temps real.
Esta tesis estudia la optimización paramétrica en las redes celulares y redes cell-free, explorando los paradigmas basados en datos y en expertos. La asignación y el control de la potencia, que ajustan la potencia de transmisión para cumplir con diferentes criterios de equidad como max-min o max-product, son tareas cruciales en las comunicaciones inalámbricas pertenecientes a la categoría de optimización paramétrica. Los enfoques más modernos de control y asignación de la potencia suelen exigir enormes costes computacionales y no son adecuados para aplicaciones en tiempo real. Para abordar esta cuestión, desarrollamos un enfoque de aprendizaje no supervisado de propósito general que resuelve las optimizaciones paramétricas y a su vez ampliamos el reconocido algoritmo de control de potencia fraccionada. En el paradigma basado en datos, creamos un marco de aprendizaje no supervisado que define una red neuronal (NN, por sus siglas en inglés) específica, incorporando conocimiento de expertos a la función de coste de la NN para resolver los problemas de control y asignación de potencia. Dentro de este enfoque, se entrena una NN de tipo feedforward mediante el muestreo repetido del espacio de parámetros, pero, en lugar de resolver completamente el problema de optimización asociado, se toma un solo paso en la dirección del gradiente de la función objetivo. El método resultante es aplicable tanto a los problemas de optimización convexos como no convexos. Ofrece una aceleración de dos a tres órdenes de magnitud en los problemas de control y asignación de potencia, en comparación con un algoritmo de resolución convexo—siempre que sea aplicable. Dentro del paradigma dirigido por expertos, investigamos la extensión del control de potencia fraccionada a las redes cell-free. La solución de forma cerrada resultante puede ser evaluada para el enlace uplink y el downlink sin esfuerzo y alcanza una solución (casi) óptima en el caso del enlace uplink. En ambos paradigmas, nos centramos especialmente en las large-scale gains— la cantidad de atenuación que experimenta la potencia media local recibida. La naturaleza lenta y variable de las ganancias a gran escala relaja la necesidad de una actualización frecuente de las soluciones tanto en el paradigma basado en datos como en el basado en expertos, permitiendo el uso de ambos métodos en aplicaciones en tiempo real.
Vendramin, Nicoló. "Unsupervised Anomaly Detection on Multi-Process Event Time Series". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254885.
Pełny tekst źródłaAtt fastställa huruvida observerade data är avvikande eller inte är en viktig uppgift som har studerats ingående i litteraturen och problemet blir ännu mer komplext, om detta kombineras med högdimensionella representationer och flera källor som oberoende genererar de mönster som ska analyseras. Arbetet som presenteras i denna uppsats använder en data-driven pipeline för definitionen av en återkommande auto-encoderarkitektur för att analysera, på ett oövervakat sätt, högdimensionella händelsetidsserier som genereras av flera och variabla processer som interagerar med ett system. Mot bakgrund av ovanstående problem undersöker arbetet om det är möjligt eller inte att använda en enda modell för att analysera mönster som producerats av olika källor. Analys av loggfiler som registrerar händelser av interaktion mellan användare och radionätverksinfrastruktur används som en fallstudie för det angivna problemet. Undersökningen syftar till att verifiera prestandan hos en enda maskininlärningsmodell som tillämpas för inlärning av flera mönster som utvecklats över tid från olika källor. Arbetet föreslår en pipeline för att hantera den komplexa representationen hos datakällorna och definitionen och avstämningen av anomalidetektionsmodellen, som inte är baserad på domänspecifik kunskap och därför kan anpassas till olika probleminställningar. Modellen har implementerats i fyra olika varianter som har utvärderats med avseende på både normala och avvikande data, som delvis har samlats in från verkliga nätverksceller och delvis från simulering av avvikande beteenden. De empiriska resultaten visar modellens tillämplighet för detektering av avvikande sekvenser och händelser i det föreslagna ramverket, med F1-score över 80%, varierande beroende på den specifika tröskelinställningen. Dessutom ger deras djupare tolkning insikter om skillnaden mellan olika varianter av modellen och därmed deras begränsningar och styrkor.
Ayodele, Taiwo Oladipupo. "An integrated framework for solving email management problems with unsupervised machine learning techniques and artificial neural networks". Thesis, University of Portsmouth, 2010. https://researchportal.port.ac.uk/portal/en/theses/an-integrated-framework-for-solving-email-management-problems-with-unsupervised-machine-learning-techniques-and-artificial-neural-networks(7bb647da-3759-47e2-812a-e1adc5e36af0).html.
Pełny tekst źródłaKilinc, Ismail Ozsel. "Graph-based Latent Embedding, Annotation and Representation Learning in Neural Networks for Semi-supervised and Unsupervised Settings". Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7415.
Pełny tekst źródłaSchneider, C. "Using unsupervised machine learning for fault identification in virtual machines". Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.
Pełny tekst źródłaBoschini, Matteo. "Unsupervised Learning of Scene Flow". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16226/.
Pełny tekst źródłaHalsey, Phillip A. "The Nature of Modality and Learning Task: Unsupervised Learning of Auditory Categories". Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1433406793.
Pełny tekst źródłaDonati, Lorenzo. "Domain Adaptation through Deep Neural Networks for Health Informatics". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14888/.
Pełny tekst źródłaLin, Brian K. "An unsupervised neural network fault discriminating system implementation for on-line condition monitoring and diagnostics of induction machines". Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/14957.
Pełny tekst źródłaBelharbi, Soufiane. "Neural networks regularization through representation learning". Thesis, Normandie, 2018. http://www.theses.fr/2018NORMIR10/document.
Pełny tekst źródłaNeural network models and deep models are one of the leading and state of the art models in machine learning. They have been applied in many different domains. Most successful deep neural models are the ones with many layers which highly increases their number of parameters. Training such models requires a large number of training samples which is not always available. One of the fundamental issues in neural networks is overfitting which is the issue tackled in this thesis. Such problem often occurs when the training of large models is performed using few training samples. Many approaches have been proposed to prevent the network from overfitting and improve its generalization performance such as data augmentation, early stopping, parameters sharing, unsupervised learning, dropout, batch normalization, etc. In this thesis, we tackle the neural network overfitting issue from a representation learning perspective by considering the situation where few training samples are available which is the case of many real world applications. We propose three contributions. The first one presented in chapter 2 is dedicated to dealing with structured output problems to perform multivariate regression when the output variable y contains structural dependencies between its components. Our proposal aims mainly at exploiting these dependencies by learning them in an unsupervised way. Validated on a facial landmark detection problem, learning the structure of the output data has shown to improve the network generalization and speedup its training. The second contribution described in chapter 3 deals with the classification task where we propose to exploit prior knowledge about the internal representation of the hidden layers in neural networks. This prior is based on the idea that samples within the same class should have the same internal representation. We formulate this prior as a penalty that we add to the training cost to be minimized. Empirical experiments over MNIST and its variants showed an improvement of the network generalization when using only few training samples. Our last contribution presented in chapter 4 showed the interest of transfer learning in applications where only few samples are available. The idea consists in re-using the filters of pre-trained convolutional networks that have been trained on large datasets such as ImageNet. Such pre-trained filters are plugged into a new convolutional network with new dense layers. Then, the whole network is trained over a new task. In this contribution, we provide an automatic system based on such learning scheme with an application to medical domain. In this application, the task consists in localizing the third lumbar vertebra in a 3D CT scan. A pre-processing of the 3D CT scan to obtain a 2D representation and a post-processing to refine the decision are included in the proposed system. This work has been done in collaboration with the clinic "Rouen Henri Becquerel Center" who provided us with data
Yuan, Xiao. "Graph neural networks for spatial gene expression analysis of the developing human heart". Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-427330.
Pełny tekst źródłaOquab, Maxime. "Convolutional neural networks : towards less supervision for visual recognition". Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE061.
Pełny tekst źródłaConvolutional Neural Networks are flexible learning algorithms for computer vision that scale particularly well with the amount of data that is provided for training them. Although these methods had successful applications already in the ’90s, they were not used in visual recognition pipelines because of their lesser performance on realistic natural images. It is only after the amount of data and the computational power both reached a critical point that these algorithms revealed their potential during the ImageNet challenge of 2012, leading to a paradigm shift in visual recogntion. The first contribution of this thesis is a transfer learning setup with a Convolutional Neural Network for image classification. Using a pre-training procedure, we show that image representations learned in a network generalize to other recognition tasks, and their performance scales up with the amount of data used in pre-training. The second contribution of this thesis is a weakly supervised setup for image classification that can predict the location of objects in complex cluttered scenes, based on a dataset indicating only with the presence or absence of objects in training images. The third contribution of this thesis aims at finding possible paths for progress in unsupervised learning with neural networks. We study the recent trend of Generative Adversarial Networks and propose two-sample tests for evaluating models. We investigate possible links with concepts related to causality, and propose a two-sample test method for the task of causal discovery. Finally, building on a recent connection with optimal transport, we investigate what these generative algorithms are learning from unlabeled data
Candadai, Vasu Madhavun. "ANSWER : A Cognitively-Inspired System for the Unsupervised Detection of Semantically Salient Words in Texts". University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439305439.
Pełny tekst źródłaMcClintick, Kyle W. "Training Data Generation Framework For Machine-Learning Based Classifiers". Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1276.
Pełny tekst źródłaCherti, Mehdi. "Deep generative neural networks for novelty generation : a foundational framework, metrics and experiments". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS029/document.
Pełny tekst źródłaIn recent years, significant advances made in deep neural networks enabled the creation of groundbreaking technologies such as self-driving cars and voice-enabled personal assistants. Almost all successes of deep neural networks are about prediction, whereas the initial breakthroughs came from generative models. Today, although we have very powerful deep generative modeling techniques, these techniques are essentially being used for prediction or for generating known objects (i.e., good quality images of known classes): any generated object that is a priori unknown is considered as a failure mode (Salimans et al., 2016) or as spurious (Bengio et al., 2013b). In other words, when prediction seems to be the only possible objective, novelty is seen as an error that researchers have been trying hard to eliminate. This thesis defends the point of view that, instead of trying to eliminate these novelties, we should study them and the generative potential of deep nets to create useful novelty, especially given the economic and societal importance of creating new objects in contemporary societies. The thesis sets out to study novelty generation in relationship with data-driven knowledge models produced by deep generative neural networks. Our first key contribution is the clarification of the importance of representations and their impact on the kind of novelties that can be generated: a key consequence is that a creative agent might need to rerepresent known objects to access various kinds of novelty. We then demonstrate that traditional objective functions of statistical learning theory, such as maximum likelihood, are not necessarily the best theoretical framework for studying novelty generation. We propose several other alternatives at the conceptual level. A second key result is the confirmation that current models, with traditional objective functions, can indeed generate unknown objects. This also shows that even though objectives like maximum likelihood are designed to eliminate novelty, practical implementations do generate novelty. Through a series of experiments, we study the behavior of these models and the novelty they generate. In particular, we propose a new task setup and metrics for selecting good generative models. Finally, the thesis concludes with a series of experiments clarifying the characteristics of models that can exhibit novelty. Experiments show that sparsity, noise level, and restricting the capacity of the net eliminates novelty and that models that are better at recognizing novelty are also good at generating novelty
Parkinson, Jon. "Representation learning with a temporally coherent mixed-representation". Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/representation-learning-with-a-temporally-coherent-mixedrepresentation(ba48bd9e-80ed-4d37-b743-cb149bc498ee).html.
Pełny tekst źródłaMathur, Nitin O. "Application of Autoencoder Ensembles in Anomaly and Intrusion Detection using Time-Based Analysis". University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin161374876195402.
Pełny tekst źródłaLi, Yingzhen. "Approximate inference : new visions". Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/277549.
Pełny tekst źródłaHassan, K. J. "Application of artificial neural networks for understanding and diagnosing the state of mastitis in dairy cattle". Lincoln University, 2007. http://hdl.handle.net/10182/633.
Pełny tekst źródłaSimões, Alexandre da Silva. "Aprendizado não-supervisionado em redes neurais pulsadas de base radial". Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-15092006-153353/.
Pełny tekst źródłaPulsed neural networks - networks that encode information in the timing of spikes - have been studied as a new and promising approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Recently, a new method for encoding input-data by population code using gaussian receptive fields has showed interesting results in the clustering task. The present work proposes a function for the unsupervised learning task in this network, which goal includes the simplification of the calibration of the network key parameters and the enhancement of the pulsed neural network convergence to instance based learning. The performance of this model is evaluated for pattern classification, particularly for the pixel colors classification task, in the computer vision domain.
Dekhtiar, Jonathan. "Deep Learning and unsupervised learning to automate visual inspection in the manufacturing industry". Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2513.
Pełny tekst źródłaAlthough studied since 1970, automatic visual inspection on production lines still struggles to be applied on a large scale and at low cost. The methods used depend greatly on the availability of domain experts. This inevitably leads to increased costs and reduced flexibility in the methods used. Since 2012, advances in the field of Deep Learning have enabled many advances in this direction, particularly thanks to convolutional neura networks that have achieved near-human performance in many areas associated with visual perception (e.g. object recognition and detection, etc.). This thesis proposes an unsupervised approach to meet the needs of automatic visual inspection. This method, called AnoAEGAN, combines adversarial learning and the estimation of a probability density function. These two complementary approaches make it possible to jointly estimate the pixel-by-pixel probability of a visual defect on an image. The model is trained from a very limited number of images (i.e. less than 1000 images) without using expert knowledge to "label" the data beforehand. This method allows increased flexibility with a limited training time and therefore great versatility, demonstrated on ten different tasks without any modification of the model. This method should reduce development costs and the time required to deploy in production. This method can also be deployed in a complementary way to a supervised approach in order to benefit from the advantages of each approach
Choi, Jin-Woo. "Action Recognition with Knowledge Transfer". Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/101780.
Pełny tekst źródłaDoctor of Philosophy
Recent progress on deep learning has shown remarkable action recognition performance. The remarkable performance is often achieved by transferring the knowledge learned from existing large-scale data to the small-scale data specific to applications. However, existing action recog- nition models do not always work well on new tasks and datasets because of the following two problems. i) Current action recognition datasets have a spurious correlation between action types and background scene types. The models trained on these datasets are biased towards the scene instead of focusing on the actual action. This scene bias leads to poor performance on the new datasets and tasks. ii) Directly testing the model trained on the source data on the target data leads to poor performance as the source, and target distributions are different. Fine-tuning the model on the target data can mitigate this issue. However, manual labeling small-scale target videos is labor-intensive. In this dissertation, I propose solutions to these two problems. To tackle the first problem, I propose to learn scene-invariant action representations to mitigate background scene- biased human action recognition models for the first problem. Specifically, the proposed method learns representations that cannot predict the scene types and the correct actions when there is no evidence. I validate the proposed method's effectiveness by transferring the pre-trained model to multiple action understanding tasks. The results show consistent improvement over the baselines for every task and dataset. To handle the second problem, I formulate human action recognition as an unsupervised learning problem on the target data. In this setting, we have many labeled videos as source data and unlabeled videos as target data. We can use already existing labeled video datasets as source data in this setting. The task is to align the source and target feature distributions so that the learned model can generalize well on the target data. I propose 1) aligning the more important temporal part of each video and 2) encouraging the model to focus on action, not the background scene. The proposed method is simple and intuitive while achieving state-of-the-art performance without training on a lot of labeled target videos. I relax the unsupervised target data setting to a sparsely labeled target data setting. Here, we have many labeled videos as source data and sparsely labeled videos as target data. The setting is practical as sometimes we can afford a little bit of cost for labeling target data. I propose multiple video data augmentation methods to inject color, spatial, temporal, and scene invariances to the action recognition model in this setting. The resulting method shows favorable performance on the public benchmarks.
cruz, Magnus Alencar da. "AvaliaÃÃo de redes neurais competitivas em tarefas de quantizaÃÃo vetorial:um estudo comparativo". Universidade Federal do CearÃ, 2007. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=2016.
Pełny tekst źródłaEsta dissertaÃÃo tem como principal meta realizar um estudo comparativo do desempenho de algoritmos de redes neurais competitivas nÃo-supervisionadas em problemas de quantizaÃÃo vetorial (QV) e aplicaÃÃes correlatas, tais como anÃlise de agrupamentos (clustering) e compressÃo de imagens. A motivaÃÃo para tanto parte da percepÃÃo de que hà uma relativa escassez de estudos comparativos sistemÃticos entre algoritmos neurais e nÃo-neurais de anÃlise de agrupamentos na literatura especializada. Um total de sete algoritmos sÃo avaliados, a saber: algoritmo K -mÃdias e as redes WTA, FSCL, SOM, Neural-Gas, FuzzyCL e RPCL. De particular interesse à a seleÃÃo do nÃmero Ãtimo de neurÃnios. NÃo hà um mÃtodo que funcione para todas as situaÃÃes, restando portanto avaliar a influÃncia que cada tipo de mÃtrica exerce sobre algoritmo em estudo. Por exemplo, os algoritmos de QV supracitados sÃo bastante usados em tarefas de clustering. Neste tipo de aplicaÃÃo, a validaÃÃo dos agrupamentos à feita com base em Ãndices que quantificam os graus de compacidade e separabilidade dos agrupamentos encontrados, tais como Ãndice Dunn e Ãndice Davies-Bouldin (DB). Jà em tarefas de compressÃo de imagens, determinado algoritmo de QV à avaliado em funÃÃo da qualidade da informaÃÃo reconstruÃda, daà as mÃtricas mais usadas serem o erro quadrÃtico mÃdio de quantizaÃÃo (EQMQ) ou a relaÃÃo sinal-ruÃdo de pico (PSNR). Empiricamente verificou-se que, enquanto o Ãndice DB favorece arquiteturas com poucos protÃtipos e o Dunn com muitos, as mÃtricas EQMQ e PSNR sempre favorecem nÃmeros ainda maiores. Nenhuma das mÃtricas supracitadas leva em consideraÃÃo o nÃmero de parÃmetros do modelo. Em funÃÃo disso, esta dissertaÃÃo propÃe o uso do critÃrio de informaÃÃo de Akaike (AIC) e o critÃrio do comprimento descritivo mÃnimo (MDL) de Rissanen para selecionar o nÃmero Ãtimo de protÃtipos. Este tipo de mÃtrica mostra-se Ãtil na busca do nÃmero de protÃtipos que satisfaÃa simultaneamente critÃrios opostos, ou seja, critÃrios que buscam o menor erro de reconstruÃÃo a todo custo (MSE e PSNR) e critÃrios que buscam clusters mais compactos e coesos (Ãndices Dunn e DB). Como conseqÃÃncia, o nÃmero de protÃtipos obtidos pelas mÃtricas AIC e MDL à geralmente um valor intermediÃrio, i.e. nem tÃo baixo quanto o sugerido pelos Ãndices Dunn e DB, nem tÃo altos quanto o sugerido pelas mÃtricas MSE e PSNR. Outra conclusÃo importante à que nÃo necessariamente os algoritmos mais sofisticados do ponto de vista da modelagem, tais como as redes SOM e Neural-Gas, sÃo os que apresentam melhores desempenhos em tarefas de clustering e quantizaÃÃo vetorial. Os algoritmos FSCL e FuzzyCL sÃo os que apresentam melhores resultados em tarefas de quantizaÃÃo vetorial, com a rede FSCL apresentando melhor relaÃÃo custo-benefÃcio, em funÃÃo do seu menor custo computacional. Para finalizar, vale ressaltar que qualquer que seja o algoritmo escolhido, se o mesmo tiver seus parÃmetros devidamente ajustados e seus desempenhos devidamente avaliados, as diferenÃas de performance entre os mesmos sÃo desprezÃveis, ficando como critÃrio de desempate o custo computacional.
The main goal of this master thesis was to carry out a comparative study of the performance of algorithms of unsupervised competitive neural networks in problems of vector quantization (VQ) tasks and related applications, such as cluster analysis and image compression. This study is mainly motivated by the relative scarcity of systematic comparisons between neural and nonneural algorithms for VQ in specialized literature. A total of seven algorithms are evaluated, namely: K-means, WTA, FSCL, SOM, Neural-Gas, FuzzyCL and RPCL. Of particular interest is the problem of selecting an adequate number of neurons given a particular vector quantization problem. Since there is no widespread method that works satisfactorily for all applications, the remaining alternative is to evaluate the influence that each type of evaluation metric has on a specific algorithm. For example, the aforementioned vector quantization algorithms are widely used in clustering-related tasks. For this type of application, cluster validation is based on indexes that quantify the degrees of compactness and separability among clusters, such as the Dunn Index and the Davies- Bouldin (DB) Index. In image compression tasks, however, a given vector quantization algorithm is evaluated in terms of the quality of the reconstructed information, so that the most used evaluation metrics are the mean squared quantization error (MSQE) and the peak signal-to-noise ratio (PSNR). This work verifies empirically that, while the indices Dunn and DB or favors architectures with many prototypes (Dunn) or with few prototypes (DB), metrics MSE and PSNR always favor architectures with well bigger amounts. None of the evaluation metrics cited previously takes into account the number of parameters of the model. Thus, this thesis evaluates the feasibility of the use of the Akaikeâs information criterion (AIC) and Rissanenâs minimum description length (MDL) criterion to select the optimal number of prototypes. This type of evaluation metric indeed reveals itself useful in the search of the number of prototypes that simultaneously satisfies conflicting criteria, i.e. those favoring more compact and cohesive clusters (Dunn and DB indices) versus those searching for very low reconstruction errors (MSE and PSNR). Thus, the number of prototypes suggested by AIC and MDL is generally an intermediate value, i.e nor so low as much suggested for the indexes Dunn and DB, nor so high as much suggested one for metric MSE and PSNR. Another important conclusion is that sophisticated models, such as the SOM and Neural- Gas networks, not necessarily have the best performances in clustering and VQ tasks. For example, the algorithms FSCL and FuzzyCL present better results in terms of the the of the reconstructed information, with the FSCL presenting better cost-benefit ratio due to its lower computational cost. As a final remark, it is worth emphasizing that if a given algorithm has its parameters suitably tuned and its performance fairly evaluated, the differences in performance compared to others prototype-based algorithms is minimum, with the coputational cost being used to break ties.
Cherif, Aymen. "Réseaux de neurones, SVM et approches locales pour la prévision de séries temporelles". Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4003/document.
Pełny tekst źródłaTime series forecasting is a widely discussed issue for many years. Researchers from various disciplines have addressed it in several application areas : finance, medical, transportation, etc. In this thesis, we focused on machine learning methods : neural networks and SVM. We have also been interested in the meta-methods to push up the predictor performances, and more specifically the local models. In a divide and conquer strategy, the local models perform a clustering over the data sets before different predictors are affected into each obtained subset. We present in this thesis a new algorithm for recurrent neural networks to use them as local predictors. We also propose two novel clustering techniques suitable for local models. The first is based on Kohonen maps, and the second is based on binary trees
Stella, Federico. "Learning a Local Reference Frame for Point Clouds using Spherical CNNs". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20197/.
Pełny tekst źródłaNavarro, Marco Vin?cius Monteiro. "Emprego de redes neurais artificiais supervisionadas e n?o supervisionadas no estudo de par?metros reol?gicos de excipientes farmac?uticos s?lidos". Universidade Federal do Rio Grande do Norte, 2014. http://repositorio.ufrn.br:8080/jspui/handle/123456789/13866.
Pełny tekst źródłaIn this paper artificial neural network (ANN) based on supervised and unsupervised algorithms were investigated for use in the study of rheological parameters of solid pharmaceutical excipients, in order to develop computational tools for manufacturing solid dosage forms. Among four supervised neural networks investigated, the best learning performance was achieved by a feedfoward multilayer perceptron whose architectures was composed by eight neurons in the input layer, sixteen neurons in the hidden layer and one neuron in the output layer. Learning and predictive performance relative to repose angle was poor while to Carr index and Hausner ratio (CI and HR, respectively) showed very good fitting capacity and learning, therefore HR and CI were considered suitable descriptors for the next stage of development of supervised ANNs. Clustering capacity was evaluated for five unsupervised strategies. Network based on purely unsupervised competitive strategies, classic "Winner-Take-All", "Frequency-Sensitive Competitive Learning" and "Rival-Penalize Competitive Learning" (WTA, FSCL and RPCL, respectively) were able to perform clustering from database, however this classification was very poor, showing severe classification errors by grouping data with conflicting properties into the same cluster or even the same neuron. On the other hand it could not be established what was the criteria adopted by the neural network for those clustering. Self-Organizing Maps (SOM) and Neural Gas (NG) networks showed better clustering capacity. Both have recognized the two major groupings of data corresponding to lactose (LAC) and cellulose (CEL). However, SOM showed some errors in classify data from minority excipients, magnesium stearate (EMG) , talc (TLC) and attapulgite (ATP). NG network in turn performed a very consistent classification of data and solve the misclassification of SOM, being the most appropriate network for classifying data of the study. The use of NG network in pharmaceutical technology was still unpublished. NG therefore has great potential for use in the development of software for use in automated classification systems of pharmaceutical powders and as a new tool for mining and clustering data in drug development
Neste trabalho foram estudadas redes neurais artificiais (RNAs) baseadas em algoritmos supervisionados e n?o supervisionados para emprego no estudo de par?metros reol?gicos de excipientes farmac?uticos s?lidos, visando desenvolver ferramentas computacionais para o desenvolvimento de formas farmac?uticas s?lidas. Foram estudadas quatro redes neurais artificiais supervisionadas e cinco n?o supervisionadas. Todas as RNAs supervisionadas foram baseadas em arquitetura de rede perceptron multicamada alimentada ? frente (feedfoward MLP). Das cinco RNAs n?o supervisionadas, tr?s foram baseadas em estrat?gias puramente competitivas, "Winner-Take- All" cl?ssica, "Frequency-Sensitive Competitive Learning" e "Rival-Penalize Competitive Learning" (WTA, FSCL e RPCL, respectivamente). As outras duas redes n?o supervisionadas, Self- Organizing Map e Neural Gas (SOM e NG) foram baseadas estrat?gias competitivo-cooperativas. O emprego da rede NG em tecnologia farmac?utica ? ainda in?dito e pretende-se avaliar seu potencial de emprego como nova ferramenta de minera??o e classifica??o de dados no desenvolvimento de medicamentos. Entre os prot?tipos de RNAs supervisionadas o melhor desempenho foi conseguido com uma rede de arquitetura composta por 8 neur?nios de entrada, 16 neur?nios escondidos e 1 neur?nio de sa?da. O aprendizado de rede e a capacidade preditiva em rela??o ao ?ngulo de repouso (α) foi deficiente, e muito boa para o ?ndice de Carr e fator de Hausner (IC, FH). Por esse motivo IC e FH foram considerados bons descritores para uma pr?xima etapa de desenvolvimento das RNAs supervisionadas. As redes, WTA, RPCL e FSCL, foram capazes de estabelecer agrupamentos dentro da massa de dados, por?m apresentaram erros grosseiros de classifica??o caracterizados pelo agrupamento de dados com propriedades conflitantes, e tamb?m n?o foi poss?vel estabelecer qual o crit?rio de classifica??o adotado. Tais resultados demonstraram a inviabilidade pr?tica dessas redes para os sistemas estudados sob nossas condi??es experimentais. As redes SOM e NG mostraram uma capacidade de classifica??o muito superior ?s RNAs puramente competitivas. Ambas as redes reconheceram os dois agrupamentos principais de dados correspondentes ? lactose (LAC) e celulose (CEL). Entretanto a rede som demonstrou defici?ncia na classifica??o de dados relativos aos excipientes minorit?rios, estearato de magn?sio (EMG), talco (TLC) e atapulgita (ATP). A rede NG, por sua vez, estabeleceu uma classifica??o muito consistente dos dados e resolveu o erro de classifica??o apresentados pela rede SOM, mostrando-se a rede mais adequada para a classifica??o dos dado do presente estudo. A rede Neural Gas, portanto, mostrou- se promissora para o desenvolvimento de softwares para uso na classifica??o automatizada de sistemas pulverulentos farmac?uticos
Nair, Karthik. "Optimisation of autoencoders for prediction of SNPs determining phenotypes in wheat". Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-437451.
Pełny tekst źródłaMcConnell, Sabine. "An unsupervised neural network for the clustering of extragalactic objects". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2002. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ65638.pdf.
Pełny tekst źródłaESTEU, BRUNO ROMANELLI MENECHINI. "CLUSTERING VIBRATION DATA FROM OIL WELLS THROUGH UNSUPERVISED NEURAL NETWORK". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25049@1.
Pełny tekst źródłaCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
A perfuração de poços de petróleo em águas profundas tem como objetivo atingir o melhor ponto de extração de óleo e gás natural presentes em reservatórios a alguns milhares de metros no fundo do mar. Um melhor entendimento da dinâmica de perfuração através da análise de parâmetros operacionais em tempo real é importante para otimizar os processos de perfuração e reduzir seus tempos de operação. Com esse objetivo, operadoras de petróleo têm realizado grandes investimentos no desenvolvimento de ferramentas de medição e transmissão de parâmetros durante a perfuração, tais como, entre outros, o peso sobre broca, rotação da coluna e vazão do fluido de perfuração. Dentre as vantagens em se monitorar estes dados em tempo real, destaca-se a otimização de parâmetros operacionais buscando obter uma taxa de penetração satisfatória com o menor gasto de energia possível. Em uma perfuração rotativa, essa energia é muitas vezes parcialmente dissipada devido à vibração da coluna causada pela interação entre broca e formação. Nesta dissertação, com o objetivo de extrair características comuns que pudessem vir a ajudar na otimização da atividade de perfuração, foi utilizada uma técnica de redes neurais não supervisionadas para análise de uma extensa base de dados levantados ao longo de campanhas de perfuração de poços em um mesmo campo de petróleo. Os dados de campo analisados foram obtidos ao longo de perfurações de poços verticais, exclusivamente empregando brocas tipo PDC e exibindo elevados níveis de vibração torcional. O estudo realizado a partir de registros de parâmetros de perfuração, características dos poços e respostas de vibração obtidas em tempo real por ferramentas de poço, e empregando o código de mineração de dados WEKA e a plataforma computacional de análise TIBCO Spotfire, permitiu a determinação de uma curva de desgaste de broca e a influência das ferramentas de navegação no nível de severidade de vibração ao longo da perfuração.
Drilling oil wells in deep waters aims to achieve the best point of extraction of oil and natural gas reservoirs present in a few thousand meters in the seabed. A better understanding of the drilling dynamics through the analysis of real time operation parameters is important to optimize drilling process and reduce operation time. For this purpose petroleum operator companies have been made great investments in developing tools that measure and transmit parameters during drilling operation, such as the weight on bit, pipes rotation per minute and drilling fluid flow. Among the advantages to monitor this real time data there is the operational parameters optimization looking for the least expenditure of energy as possible. In a rotary drilling operation this energy is often lost partially due to column vibration caused by the interaction between bit and formation.In this master s thesis in order to extract common features that could help on the drilling operation optimization a technique using unsupervised neural networks for analyze an extensive database which was built over drilling campaigns in a big oil field . The field data analyzed were obtained during drilling vertical wells exclusively employing PDC bits and presented high levels of torcional vibration. The study was made from drilling parameters records, wells characteristics and vibration responses obtained in real time by downhole tools. Employing the WEKA data mining code and the computing analysis platform TIBCO potfire it was possible determine a bit wear curve and the real influence of navigation tools on the severity levels of vibration during drilling operations.
CAO, BAOQIANG. "ON APPLICATIONS OF STATISTICAL LEARNING TO BIOPHYSICS". University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1168577852.
Pełny tekst źródłaMackenzie, Mathew David. "CDUL Class Directed Unsupervised Learning : an enhanced neural network classification system". Thesis, University of Kent, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360970.
Pełny tekst źródła