Дисертації з теми "Restricted Boltzmann Machine (RBM)"
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Bertholds, Alexander, and Emil Larsson. "An intelligent search for feature interactions using Restricted Boltzmann Machines." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-202208.
Повний текст джерелаKlarna använder en logistisk regression för att estimera sannolikheten att en e-handelskund inte kommer att betala sina fakturor efter att ha givits kredit. Den logistiska regressionen är en linjär modell och kan därför inte upptäcka icke-linjäriteter i datan. Målet med detta projekt har varit att utveckla ett program som kan användas för att hitta lämpliga icke-linjära interaktionsvariabler. Genom att införa dessa i den logistiska regressionen blir det möjligt att upptäcka icke-linjäriteter i datan och därmed förbättra sannolikhetsestimaten. Det utvecklade programmet använder Restricted Boltzmann Machines, en typ av oövervakat neuralt nätverk, vars dolda noder kan användas för att hitta datans distribution. Genom att använda de dolda noderna i den logistiska regressionen är det möjligt att se vilka delar av distributionen som är viktigast i sannolikhetsestimaten. Innehållet i de dolda noderna, som motsvarar olika delar av datadistributionen, kan användas för att hitta lämpliga interaktionsvariabler. Det var möjligt att hitta datans distribution genom att använda en Restricted Boltzmann Machine och dess dolda noder förbättrade sannolikhetsestimaten från den logistiska regressionen. De dolda noderna kunde användas för att skapa interaktionsvariabler som förbättrar Klarnas interna kreditriskmodeller.
Moody, John Matali. "Process monitoring with restricted Boltzmann machines." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86467.
Повний текст джерелаENGLISH ABSTRACT: Process monitoring and fault diagnosis are used to detect abnormal events in processes. The early detection of such events or faults is crucial to continuous process improvement. Although principal component analysis and partial least squares are widely used for process monitoring and fault diagnosis in the metallurgical industries, these models are linear in principle; nonlinear approaches should provide more compact and informative models. The use of auto associative neural networks or auto encoders provide a principled approach for process monitoring. However, until very recently, these multiple layer neural networks have been difficult to train and have therefore not been used to any significant extent in process monitoring. With newly proposed algorithms based on the pre-training of the layers of the neural networks, it is now possible to train neural networks with very complex structures, i.e. deep neural networks. These neural networks can be used as auto encoders to extract features from high dimensional data. In this study, the application of deep auto encoders in the form of Restricted Boltzmann machines (RBM) to the extraction of features from process data is considered. These networks have mostly been used for data visualization to date and have not been applied in the context of fault diagnosis or process monitoring as yet. The objective of this investigation is therefore to assess the feasibility of using Restricted Boltzmann machines in various fault detection schemes. The use of RBM in process monitoring schemes will be discussed, together with the application of these models in automated control frameworks.
AFRIKAANSE OPSOMMING: Prosesmonitering en fout diagnose word gebruik om abnormale gebeure in prosesse op te spoor. Die vroeë opsporing van sulke gebeure of foute is noodsaaklik vir deurlopende verbetering van prosesse. Alhoewel hoofkomponent-analise en parsiële kleinste kwadrate wyd gebruik word vir prosesmonitering en fout diagnose in die metallurgiese industrieë, is hierdie modelle lineêr in beginsel; nie-lineêre benaderings behoort meer kompakte en insiggewende modelle te voorsien. Die gebruik van outo-assosiatiewe neurale netwerke of outokodeerders bied 'n beginsel gebaseerder benadering om dit te bereik. Hierdie veelvoudige laag neurale netwerke was egter tot onlangs moeilik om op te lei en is dus nie tot ʼn beduidende mate in die prosesmonitering gebruik nie. Nuwe, voorgestelde algoritmes, gebaseer op voorafopleiding van die lae van die neurale netwerke, maak dit nou moontlik om neurale netwerke met baie ingewikkelde strukture, d.w.s. diep neurale netwerke, op te lei. Hierdie neurale netwerke kan gebruik word as outokodeerders om kenmerke van hoë-dimensionele data te onttrek. In hierdie studie word die toepassing van diep outokodeerders in die vorm van Beperkte Boltzmann Masjiene vir die onttrekking van kenmerke van proses data oorweeg. Tot dusver is hierdie netwerke meestal vir data visualisering gebruik en dit is nog nie toegepas in die konteks van fout diagnose of prosesmonitering nie. Die doel van hierdie ondersoek is dus om die haalbaarheid van die gebruik van Beperkte Boltzmann Masjiene in verskeie foutopsporingskemas te assesseer. Die gebruik van Beperkte Boltzmann Masjiene se eienskappe in prosesmoniteringskemas sal bespreek word, tesame met die toepassing van hierdie modelle in outomatiese beheer raamwerke.
McCoppin, Ryan R. "An Evolutionary Approximation to Contrastive Divergence in Convolutional Restricted Boltzmann Machines." Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1418750414.
Повний текст джерелаVrábel, Jakub. "Popis Restricted Boltzmann machine metody ve vztahu se statistickou fyzikou a jeho následné využití ve zpracování spektroskopických dat." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-402522.
Повний текст джерелаSvoboda, Jiří. "Multi-modální "Restricted Boltzmann Machines"." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236426.
Повний текст джерелаFredriksson, Gustav, and Anton Hellström. "Restricted Boltzmann Machine as Recommendation Model for Venture Capital." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252703.
Повний текст джерелаIn this thesis, we introduce restricted Boltzmann machines (RBMs) as a recommendation model in the context of venture capital. A network of connections is used as a proxy for investors’ preferences of companies. The main focus of the thesis is to investigate how RBMs can be implemented on a network of connections and investigate if conditional information can be used to boost RBMs. The network of connections is created by using board composition data of Swedish companies. For the network, RBMs are implemented with and without companies’ place of origin as conditional data, respectively. The RBMs are evaluated by their learning abilities and their ability to recreate withheld connections. The findings show that RBMs perform poorly when used to recreate withheld connections but can be tuned to acquire good learning abilities. Adding place of origin as conditional information improves the model significantly and show potential as a recommendation model, both with respect to learning abilities and the ability to recreate withheld connections.
Juel, Bjørn Erik. "Investigating the Consistency and Convexity of Restricted Boltzmann Machine Learning." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for nevromedisin, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-25696.
Повний текст джерелаTubiana, Jérôme. "Restricted Boltzmann machines : from compositional representations to protein sequence analysis." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE039/document.
Повний текст джерелаRestricted Boltzmann machines (RBM) are graphical models that learn jointly a probability distribution and a representation of data. Despite their simple architecture, they can learn very well complex data distributions such the handwritten digits data base MNIST. Moreover, they are empirically known to learn compositional representations of data, i.e. representations that effectively decompose configurations into their constitutive parts. However, not all variants of RBM perform equally well, and little theoretical arguments exist for these empirical observations. In the first part of this thesis, we ask how come such a simple model can learn such complex probability distributions and representations. By analyzing an ensemble of RBM with random weights using the replica method, we have characterised a compositional regime for RBM, and shown under which conditions (statistics of weights, choice of transfer function) it can and cannot arise. Both qualitative and quantitative predictions obtained with our theoretical analysis are in agreement with observations from RBM trained on real data. In a second part, we present an application of RBM to protein sequence analysis and design. Owe to their large size, it is very difficult to run physical simulations of proteins, and to predict their structure and function. It is however possible to infer information about a protein structure from the way its sequence varies across organisms. For instance, Boltzmann Machines can leverage correlations of mutations to predict spatial proximity of the sequence amino-acids. Here, we have shown on several synthetic and real protein families that provided a compositional regime is enforced, RBM can go beyond structure and extract extended motifs of coevolving amino-acids that reflect phylogenic, structural and functional constraints within proteins. Moreover, RBM can be used to design new protein sequences with putative functional properties by recombining these motifs at will. Lastly, we have designed new training algorithms and model parametrizations that significantly improve RBM generative performance, to the point where it can compete with state-of-the-art generative models such as Generative Adversarial Networks or Variational Autoencoders on medium-scale data
Spiliopoulou, Athina. "Probabilistic models for melodic sequences." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8876.
Повний текст джерелаde, Giorgio Andrea. "A study on the similarities of Deep Belief Networks and Stacked Autoencoders." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174341.
Повний текст джерелаDahlin, Fredrik. "Investigating user behavior by analysis of gaze data : Evaluation of machine learning methods for user behavior analysis in web applications." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-190906.
Повний текст джерелаI nuläget utförs analys av användarbeteende i webbapplikationer primärt med hjälp av statistiska mått över användares beteenden på hemsidor tillsammans med personas förökad förståelse av olika typer av användare. Dessa metoder ger stor insikt i hur användare använder hemsidor men ger ingen information om vilka typer av aktiviteter användare har utfört på hemsidan. Denna rapport försöker skapa metoder för analys av användaraktiviter på hemsidor endast baserat på blickdata fångade med eye trackers. Blick data från 25 personer har samlats in under tiden de utför olika uppgifter på olika hemsidor. Två olika tekniker har utvärderats där den ena analyserar blick kartor som fångat ögonens rörelser under 10 sekunder och den andra tekniken använder sig av sekvenser av händelser för att klassificera aktiviteter. Resultaten indikerar att det går att urskilja olika typer av vanligt förekommande användaraktiviteter genom analys av blick data. Resultatet visar också att det är stor osäkerhet i prediktionerna och ytterligare arbete är nödvändigt för att finna användbara modeller.
Nair, Binu Muraleedharan. "Learning Latent Temporal Manifolds for Recognition and Prediction of Multiple Actions in Streaming Videos using Deep Networks." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429532297.
Повний текст джерелаJin, Wenjing. "Modeling of Machine Life Using Accelerated Prognostics and Health Management (APHM) and Enhanced Deep Learning Methodology." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479821186023747.
Повний текст джерелаDupuy, Nathalie. "Neurocomputational model for learning, memory consolidation and schemas." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33144.
Повний текст джерелаCôté, Marc-Alexandre. "Réseaux de neurones génératifs avec structure." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10489.
Повний текст джерелаSchneider, C. "Using unsupervised machine learning for fault identification in virtual machines." Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.
Повний текст джерелаPasa, Luca. "Linear Models and Deep Learning: Learning in Sequential Domains." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3425865.
Повний текст джерелаCon la diffusione di dispositivi a basso costo, e reti di sensori (come ad esempio l'Internet of Things), nonché lo sviluppo di interfacce di interazione uomo-macchina a basso costo, la capacità di processare dati sequenziali in maniera veloce, e assicurando un basso consumo di risorse, è diventato sempre più importante. Molti sono i compiti che trarrebbero beneficio da un avanzamento in questo ambito, dal monitoraggio e classificazione di comportamenti umani fino alla predizioni di eventi futuri. Molti dei task citati richiedono l'uso di tecniche di pattern recognition e di abilità correlate con metodi tipici dell’apprendimento automatico. Molti sono gli approcci per eseguire apprendimento su domini sequenziali proposti nel recente passato, e molti sono basati su tecniche tipiche dell'ambito del Deep Learning. I metodi di Deep Learning sono tipicamente basati su sistemi fortemente non lineari, capaci di ottenere ottimi risultati in problemi di predizione/classificazione, ma che risultano anche essere molto costosi dal punto di vista computazionale. Quando si cerca di eseguire un compito di apprendimento su domini sequenziali, e più in generale su dati strutturati, tipicamente si ricorre all'utilizzo di sistemi non lineari. Non è però sempre vero che i task considerati richiedono modelli non lineari. Quindi il rischio è di andare ad utilizzare metodi troppo complessi, e computazionalmente costosi, per poi ottenere alla fine soluzioni che migliorano di un’epsilon (o anche no migliorano) i risultati ottenibili tramite l'utilizzo di sistemi lineari dinamici, che risultano essere molto meno costosi dal punto di vista dell'apprendimento, e del costo computazionale. L'obiettivo di questa tesi è di discutere del ruolo che i sistemi lineari dinamici possono avere nelle esecuzioni di compiti di apprendimento su dati strutturati. In questa tesi vogliamo mettere in luce le capacità dei sistemi lineari dinamici (LDS) di ottenere soluzioni molto buone ad un costo computazionale relativamente basso. Inoltre risulta interessante vedere come, nel caso in cui un sistema lineare non sia sufficiente per ottenere il risultato sperato, esso possa essere usato come base per costruire modelli più complessi, oppure possa essere utilizzato per eseguire la fase di pre-training per un modello non lineare, come ad esempio Echo State Networks (ESNs) e Recurrent Neural Networks (RNNs). Nello specifico in questa tesi è stato considerato un task di predizione dell'evento successivo, data una sequenza di eventi. I dataset usati per testare i vari modelli proposti nella tesi, contengono sequenze di musica polifonica, che risultano essere particolarmente lunghe e complesse. Nella prima parte della tesi viene proposto l'utilizzo del semplice modello LDS per affrontare il compito considerato. In particolare vengono considerati tre approcci diversi per eseguire l'apprendimento con questo modello. Viene poi introdotti nuovi modelli, ispirati al modello LDS, che hanno l'obiettivo di migliorare le prestazioni di quest'ultimo nei compiti di predizione/classificazione. Vengono poi considerati i più comuni modelli non lineari, in particolare il modello RNN il quale risulta essere significativamente più complesso e computazionalmente costoso da utilizzare. Viene quindi empiricamente dimostrato che, almeno per quanto riguarda il compito di predizione e i dataset considerati, l'introduzione di una fase di pre-training basati su sistemi lineari porta ad un significativo miglioramento delle prestazioni e della accuratezza nell'eseguire la predizione. In particolare 2 metodi di pre-training vengono proposti, il primo chiamato pre-training via Linear Autoencoder, ed il secondo basato su Hidden Markov Models (HMMs). I risultati sperimentali suggeriscono che i sistemi lineari possono giocare un ruolo importante per quanto riguarda il compito di apprendimento in domini sequenziali, sia che siano direttamente usati oppure siano usati indirettamente (come base per eseguire la fase di pre-training): infatti, usandoli direttamente, essi hanno permesso di raggiungere risultati che rappresentano lo stato dell'arte, andando però a richiedere uno sforzo computazionale molto limitato se confrontato con i più comuni modelli non lineari. Inoltre, anche quando le performance ottenute sono risultate non soddisfacenti, si è dimostrato che è possibile utilizzarli con successo per eseguire la fase di pre-training di sistemi non lineari.
Habrnál, Matěj. "Hluboké neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236132.
Повний текст джерелаda, Costa Joel. "Online Non-linear Prediction of Financial Time Series Patterns." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32221.
Повний текст джерелаYogeswaran, Arjun. "Self-Organizing Neural Visual Models to Learn Feature Detectors and Motion Tracking Behaviour by Exposure to Real-World Data." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37096.
Повний текст джерелаHubený, Marek. "Koncepty strojového učení pro kategorizaci objektů v obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316388.
Повний текст джерелаTsai, Chang-Hung, and 蔡長宏. "Restricted Boltzmann Machine (RBM) Processor Design for Neural Network and Machine Learning Applications." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/55826222299703019418.
Повний текст джерела國立交通大學
電子研究所
105
Recently, machine learning techniques have been widely applied to signal processing systems to support intelligent capabilities, such as AdaBoost, K-NN, mean-shift, and SVM for data classification, and HOG and SIFT for feature extraction in multimedia applications. In the past decades, the neural network (NN) algorithms are considered one of the state-of-the-art solutions in many applications, and both feature extraction and data classification are integrated and cascaded in neural networks. In the big data era, the huge dataset benefits neural network learning algorithms to train a powerful and accurate model for machine learning applications. Since the network structure becomes deeper and deeper to achieve more accurate performance for applications, the traditional neural network learning algorithm with feedforwarding and error backpropagation is inefficient to train multi-layer neural networks. Moreover, the data labeling is very expensive especially for big dataset, and how to initialize a neural network without any domain knowledge is also a crucial issue for model training. In this dissertation, a restricted Boltzmann machine (RBM) processor is designed and implemented. In the proposed RBM processor, 32 proposed RBM cores are integrated for parallel computing with the neural network structure of maximal 4k neurons per layer and 128 candidates per sample for inference. Operated in the learning mode, the batch-level parallelism is achieved for RBM model training with supervised and unsupervised learning. And the sample-level parallelism is achieved for data classification operated in the inference mode. Moreover, several features are proposed and implemented in the proposed RBM processor to save computation time, hardware cost, external memory bandwidth, and power consumption. To realize the proposed RBM processor, two implementations are designed in this dissertation. Implemented in Xilinx Virtex-7 FPGA, the proposed RBM processor is operated at 125 MHz and occupies 114.0k LUTs, 107.1k flip-flops, and 80 block memory blocks. Implemented in UMC 65nm LL RVT CMOS technology, the proposed RBM processor chip costs 2.2M gates and 128kB internal SRAM with 8.8 mm2 area to integrate 32 proposed RBM cores in 2 clusters, and the maximal operating frequency of this chip achieves 210 MHz in both learning and inference modes operated at 1.2V supply voltage. According to the measurement results, the proposed FPGA-based system prototype platform achieves 4.60G neuron weights/s (NWPS) learning performance and 3.87G NWPS inference performance for RBM model training and data classification, respectively. And the proposed RBM processor chip operated at 210MHz to achieve 4.61G NWPS and 3.86G NWPS performance with 69.50 pJ/NW and 81.20 pJ/NW energy efficiency in learning and inference modes, respectively. Compared to the software solution implemented on CPU and powerful multi-core processors, the proposed RBM processor achieves faster processing time and higher energy efficiency in both RBM model learning and data inference, respectively. Since the battery life is a crucial issue in IoT and handheld devices, our proposal achieves an energy-efficient solution to integrate the proposed RBM processor chip into the emerging energy-constrained devices to support intelligent capabilities with learning and inference for in-time model training and real-time decision making.
Pandey, Gaurav. "Deep Learning with Minimal Supervision." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4315.
Повний текст джерелаAnderson, David John. "Automatic speech feature extraction using a convolutional restricted boltzmann machine." Thesis, 2017. https://hdl.handle.net/10539/26165.
Повний текст джерелаRestricted Boltzmann Machines (RBMs) are a statistical learning concept that can be interpreted as Arti cial Neural Networks. They are capable of learning, in an unsupervised fashion, a set of features with which to describe a data set. Connected in series RBMs form a model called a Deep Belief Network (DBN), learning abstract feature combinations from lower layers. Convolutional RBMs (CRBMs) are a variation on the RBM architecture in which the learned features are kernels that are convolved across spatial portions of the input data to generate feature maps identifying if a feature is detected in a portion of the input data. Features extracted from speech audio data by a trained CRBM have recently been shown to compete with the state of the art for a number of speaker identi cation tasks. This project implements a similar CRBM architecture in order to verify previous work, as well as gain insight into Digital Signal Processing (DSP), Generative Graphical Models, unsupervised pre-training of Arti cial Neural Networks, and Machine Learning classi cation tasks. The CRBM architecture is trained on the TIMIT speech corpus and the learned features veri ed by using them to train a linear classi er on tasks such as speaker genetic sex classi cation and speaker identi cation. The implementation is quantitatively proven to successfully learn and extract a useful feature representation for the given classi cation tasks
MT 2018
Huang, Chien-Ming, and 黃建銘. "Research in Recognition Method Based on Continuous Restricted Boltzmann Machine." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/97998030304152120598.
Повний текст джерела國立清華大學
電機工程學系
102
In recent years, the biomedical application of electronic nose sensor system has been noticed, for example, this thesis will focus on the recognition of pneumonia data from patients. However, the sensitivity of sensor array is not high enough so that the captured data is somewhat overlapped. In order to analyze these data further, this thesis proposes some methods to classify them with probabilistic model, such as CRBM. Continuous Restricted Boltzmann Machine (CRBM) is a generative probabilistic model that can cluster and classify, and that can reconstruct data distribution from training data. Therefore, there are 3 possible ways to classify pneumonia data by CRBM. First, as a clusterer, CRBM can re-project data into higher -dimensional space or lower-dimensional space so that the data will be classified more easily. Secondly, as a classifier, CRBM uses an additional neuron as label to learn class of training data. Finally, as a generative model, CRBM can re-generate the data distribution of training data following its energy function so that we can estimate the probability density in the space. After estimating the probability density, the Bayesian Classifier can classify with it. In addition, this thesis proposes a setup to test 3 rd CRBM analog chip. Since training mechanism was not designed for the this chip, so we use the data acquisition (DAQ) system and FPGA card to implement training algorithm of CRBM. This is the so-called Chip-in-a-Loop training. The performance of this training mechanism will be evalutated.
Teng, Chih-Jung, and 鄧智嶸. "Training Restricted Boltzmann Machine for People Counting with PIR Sensors." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/17252842870795458226.
Повний текст джерелаUpadhya, Vidyadhar. "Efficient Algorithms for Learning Restricted Boltzmann Machines." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4840.
Повний текст джерелаTai, Chih-Yuan, and 戴志遠. "An Intelligent System for Object Recognition Using Extended Restricted Boltzmann Machine." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/q55x53.
Повний текст джерела國立臺北科技大學
電腦與通訊研究所
100
In this paper, we propose an approach that implements an intelligent system for object recognition using Extended Restricted Boltzmann Machine (ERBM). It is excellent to recognize the objects by a typical neural network, but the problem of local minima remains to be solved. Hence, the proposed method is a neural network of global minima. First, objects are segmented from the image which is captured by the camera. In order to describe many kinds of objects completely, low-level features such as shape, texture, and color are essential. Because of some noises of low-level features, the accuracy is not precise in an actual condition. The processed result is the optimum approximate solution for object classification using the trained ERBM. Finally, an inference engine outputs the intelligent explanation for the result in the designed knowledge base which can store some high-level semantic rules. From the experimental results, it is proved that the proposed method is feasible.
WANG, JEN-HUO, and 王仁和. "Design of Continuous Restricted Boltzmann Machine IC for Electronic Nose System." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/65860388450607202371.
Повний текст джерела國立清華大學
電機工程學系
102
Many portable or implantable microsystems have incorporated sensor arrays for various biomedical applications. The raw sensory signals are usually high-dimensional, noisy, and drifting. To facilitate in-situ diagnosis or to reduce the data for wireless transmission, a low-power, embedded system is demanded for fusing the sensory signals robustly in real time. A probabilistic neural network called the Continuous Restricted Boltzmann Machine (CRBM) has been shown capable of classifying biomedical data reliably. Thus, it is suitable for CRBM to act as a signal pre-processing unit in system. This paper discuss about how to use CRBM to process sensory data of electronic nose system. At first, it makes pilot simulation in software to confirm the capability of CRBM for processing sensory data. Then it will study the method of implementing CRBM into VLSI (Very Large Scale Integration) and integrating with electronic nose system. The chip of CRBM integrating with electronic nose system has been designed and fabricated with the TSMC 0.18μm and 90nm technology provided by TSMC (Taiwan Semiconductor Manufacturing Company). The measurement results proved that the CRBM hardware system can perform good processed results as expected.
Hung, Lin, and 洪琳. "Unsupervised sound summarization from an environment based on the Restricted Boltzmann Machine." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/hq5a3n.
Повний текст джерела國立清華大學
電機工程學系
105
Machine listening plays an important role in machine-human interaction applications recent years. The prospect of making the computer to imitate the learning ability of human brain also became a popular issue with the rise of neural networks. Imagine that we go to a new place where labeled sound data is not available. How to let the users know what sound events happen frequently in a period of time by applying machine learning methods? These kinds of unsupervised learning applications are relatively rare in other machine listening research. We proposed this idea and also try to use neural networks and other unsupervised algorithms to summarize sound events that happen repeatedly in a place. In the simulation experiments of our thesis, we take self-recorded audio including common indoor sounds such as people talking and object collision sounds. Two electrical alarm sounds are also designed as target sound events, which the duration of each event is less than 10% of the total recording time. Frist, we take the sound signal and apply Fourier transform, then pass through the Mel-frequency filter bank to obtain Mel-spectrogram as our feature. Restricted Boltzmann machine of neural networks is chosen as our training model. Finally, we use clustering algorithm and successfully summarize the spectrogram that happens repeatedly. The user can distinguish the two target sound events through listen to the summarized sound events.
Chen, Jyung-Ting, and 陳峻廷. "An Application of differential evolution algorithm-based restricted Boltzmann machine to recommendation systems." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/cy8m4b.
Повний текст джерела國立臺灣科技大學
工業管理系
104
Global e-commerce has grown very fast, and daily revenue can be up to billion US dollars. Many industries follow the trend and earn lots of money, such as: Amazon and Taobao. To raise revenue, Most of e-commerce’s companies endeavor to develop recommendation system to find out potential customers or stick customers. Recommendation systems can be implemented by lots of methods and the most well-known method is collaborative filtering. It mainly uses similar user’s records to recommend what similar users like. Its advantage is no need to analyze the product’s profile. This study, uses restricted Boltzmann machine (RBM) as collaborative filtering, and use differential evolution algorithm to optimize RBM’s parameter to improve prediction performance. Previously, original RBM use mini-batch gradient descent method.
Hong, Chun-Yu, and 洪昌諭. "Design of a programmable system circuit for the Continuous Restricted Boltzmann Machine in VLSI." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/57105076950350456044.
Повний текст джерелаKai-YueHong and 洪凱悅. "A Refined Sample Data Method for Hyperspectral Images Classification Based on Restricted Boltzmann Machine." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/km26m8.
Повний текст джерелаKOUTOU, Wend-Nougui Odilon, and 江歐狄. "Similarity-Boosted Hybrid Conditional Restricted Boltzmann Machine (SB H-CRBM) for Drug-Target Interaction Prediction." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/3v2pxs.
Повний текст джерела國立清華大學
資訊系統與應用研究所
106
Uncovering drug-target interactions plays a key role in the drug development process. Recently, in silico (docking simulation and machine learningbased) techniques have emerged as an alternative to costly and time consuming biochemical experiments. In machine learning-based techniques, many network-based approaches have been proposed such as Restricted Boltzmann Machine (RBM), Bipartite Local Models (BLM), Network Based Inference (NII), Weighted profile method and Advanced Local Drug-Target Interaction Prediction Technique (ALADIN). In this research, we extend the RBM by integrating important features such as drug-drug and target-target similarity. In addition, we incorporate the correlations between drugs that have not been taken into account in the original RBM. Finally, we propose a Similarity-Boosted Hybrid Conditional RBM (SB H-CRBM) which is inspired by the Content-Boosted Restricted Boltzmann Machine(CB-RBM) [1] from the recommendation systems community. Our experimental results show that our method performs better than the RBM was previously proposed by Wang and Zeng.
Su, Hong-Yi, and 蘇泓伊. "A Study of Applying Modular Restricted Boltzmann Machine to Steady-State Visual Evoked Potentials Based Brain Computer Interface." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/ejub5y.
Повний текст джерела南臺科技大學
電機工程系
106
Many patients with severe disabilities have many problems in their lives, such as inconvenience during expression and action, and it is quite difficult to use traditional assistive devices. Although there are many science and technology applications in the analysis of the human brain’s biological signals such as the Brain-Computer Interface (BCI)that there has been considerable development in related research, the accuracy of identifying brain signals is not ideal. This paper uses different statistical and spectral calculation methods combined with modules to improve the accuracy of recognizing brain signals, and ultimately apply to the relevant auxiliary equipment for patients with severe disabilities, thereby improving the quality of life of related patients. The modular restricted Boltzmann machine (MRBM) designed in this dissertation is designed to extract the characteristics of many different input parameters through multiple identification layers and a layer of decision-making layer is connected to integrate the multiple parameter features in the end. Firstly, canonical correlation analysis (CCA) was used to calculate the temporal correlation of steady-state visual evoked potentials (SSVEP). Second, the fast Fourier transform (FFT) was used to transfer the steady-state visual evoked potentials to the frequency domain. The window function is used to effectively extract the characteristics of the target frequency. Thirdly, the frequency domain correlation of the steady-state visual evoked potentials is calculated to use the magnitude squared coherence (MSC). According the each types of features, the corresponding RBM is constructed and used to identify the decision results. With these results, the decision RBM is adopted to fuse the decision detected by using different types of features and then a fused decision result can be obtained. The Restricted Boltzmann machine of the identification layer extracts the parameter characteristics. Finally, a constrained Boltzmann machine connected to a decision-making layer integrates the input of the three identification layers for decision-making and integration, and is ultimately used to determine the steady state visual evoked potentials. Keyword: modular, restricted Boltzmann machine, Brain-Computer Interface, steady-state visual evoked potentials, EEG
Susskind, Joshua Matthew. "Interpreting Faces with Neurally Inspired Generative Models." Thesis, 2011. http://hdl.handle.net/1807/29884.
Повний текст джерелаYu, Kuan-Chih, and 余觀至. "Recognition of Patients with Chronic Obstructive Pulmonary Disease by Applying Continuous Restricted Boltzmann Machine and Data-Mining Methods to Sensory Data of E-Nose." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/b32448.
Повний текст джерела國立清華大學
電機工程學系所
106
The purpose of this thesis is to the recognize Chronic Obstructive Pulmonary Disease (COPD) by applying machine-learning algorithms. In previous literature, it is confirmed that specific organic compounds are exhaled by most patients suffering from the COPD. The COPD could thus be diagnosed by using machine-learning algorithms to classify the sensory data of an electronic nose. An electronic nose (e-Nose) consists of an array of neuromorphic sensor with diversity. Each sensor exhibits its own characteristic response to different odorants. Therefore, this study aims to identify a machine-learning algorithm able to detect COPD by classifying the sensory data of an e-Nose. To ease data-classification, the following methods are employed to preprocess the e-Nose data: (1) baseline manipulation, (2) receiver operating characteristic (ROC) curve, and (3) normalization. For data classification, the performance of the following three linear classifiers are compared: (1) the support vector machine, (2) the linear discriminant analysis, (3) the linear programming. In addition, the Continuous Restricted Boltzmann Machine (CRBM) is employed as a nonlinear, probabilistic classifier. How the CRBM could improve the classification task is further explored in this thesis. Based on the fact that the CRBM learns to regenerate training data, an algorithm for estimating the likelihood of unknown data under a CRBM model is developed. This estimating algorithm enables CRBM to function as a probabilistic classifier reliably. However, our experimental results indicate that all algorithms are unable to recognize unknown data because different types of pre-processed COPD data exhibit significant overlap among each other. Further analysis indicates that sensor selection based on ROC curve filters out some important dimensions. Therefore, without the sensor selection, better classification result is achieved.
"EXPLORATION OF NEURAL CODING IN RAT'S AGRANULAR MEDIAL AND AGRANULAR LATERAL CORTICES DURING LEARNING OF A DIRECTIONAL CHOICE TASK." Doctoral diss., 2014. http://hdl.handle.net/2286/R.I.25034.
Повний текст джерелаDissertation/Thesis
Ph.D. Electrical Engineering 2014
Larochelle, Hugo. "Étude de techniques d'apprentissage non-supervisé pour l'amélioration de l'entraînement supervisé de modèles connexionnistes." Thèse, 2008. http://hdl.handle.net/1866/6435.
Повний текст джерелаLajoie, Isabelle. "Apprentissage de représentations sur-complètes par entraînement d’auto-encodeurs." Thèse, 2009. http://hdl.handle.net/1866/3768.
Повний текст джерелаProgress in the machine learning domain allows computational system to address more and more complex tasks associated with vision, audio signal or natural language processing. Among the existing models, we find the Artificial Neural Network (ANN), whose popularity increased suddenly with the recent breakthrough of Hinton et al. [22], that consists in using Restricted Boltzmann Machines (RBM) for performing an unsupervised, layer by layer, pre-training initialization, of a Deep Belief Network (DBN), which enables the subsequent successful supervised training of such architecture. Since this discovery, researchers studied the efficiency of other similar pre-training strategies such as the stacking of traditional auto-encoder (SAE) [5, 38] and the stacking of denoising auto-encoder (SDAE) [44]. This is the context in which the present study started. After a brief introduction of the basic machine learning principles and of the pre-training methods used until now with RBM, AE and DAE modules, we performed a series of experiments to deepen our understanding of pre-training with SDAE, explored its different proprieties and explored variations on the DAE algorithm as alternative strategies to initialize deep networks. We evaluated the sensitivity to the noise level, and influence of number of layers and number of hidden units on the generalization error obtained with SDAE. We experimented with other noise types and saw improved performance on the supervised task with the use of pepper and salt noise (PS) or gaussian noise (GS), noise types that are more justified then the one used until now which is masking noise (MN). Moreover, modifying the algorithm by imposing an emphasis on the corrupted components reconstruction during the unsupervised training of each different DAE showed encouraging performance improvements. Our work also allowed to reveal that DAE was capable of learning, on naturals images, filters similar to those found in V1 cells of the visual cortex, that are in essence edges detectors. In addition, we were able to verify that the learned representations of SDAE, are very good characteristics to be fed to a linear or gaussian support vector machine (SVM), considerably enhancing its generalization performance. Also, we observed that, alike DBN, and unlike SAE, the SDAE had the potential to be used as a good generative model. As well, we opened the door to novel pre-training strategies and discovered the potential of one of them : the stacking of renoising auto-encoders (SRAE).
Taylor, Graham William. "Composable, Distributed-state Models for High-dimensional Time Series." Thesis, 2009. http://hdl.handle.net/1807/19238.
Повний текст джерела