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Dissertations / Theses on the topic 'Restricted Boltzmann Machine (RBM)'

Author: Grafiati

Published: 6 September 2023

Last updated: 25 July 2025

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1

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.

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Klarna uses a logistic regression to estimate the probability that an e-store customer will default on its given credit. The logistic regression is a linear statistical model which cannot detect non-linearities in the data. The aim of this project has been to develop a program which can be used to find suitable non-linear interaction-variables. This can be achieved using a Restricted Boltzmann Machine, an unsupervised neural network, whose hidden nodes can be used to model the distribution of the data. By using the hidden nodes as new variables in the logistic regression it is possible to see

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Moody, John Matali. "Process monitoring with restricted Boltzmann machines." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86467.

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Thesis (MScEng)--Stellenbosch University, 2014.<br>ENGLISH 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 app

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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.

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Vrábel, 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.

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Práca sa zaoberá spojeniami medzi štatistickou fyzikou a strojovým učením s dôrazom na základné princípy a ich dôsledky. Ďalej sa venuje obecným vlastnostiam spektroskopických dát a ich zohľadnení pri pokročilom spracovaní dát. Začiatok práce je venovaný odvodeniu partičnej sumy štatistického systému a štúdiu Isingovho modelu pomocou "mean field" prístupu. Následne, popri základnom úvode do strojového učenia, je ukázaná ekvivalencia medzi Isingovým modelom a Hopfieldovou sieťou - modelom strojového učenia. Na konci teoretickej časti je z Hopfieldovej siete odvodený model Restricted Boltzmann M

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Svoboda, 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.

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This thesis explores how multi-modal Restricted Boltzmann Machines (RBM) can be used in content-based image tagging. This work also cointains brief analysis of modalities that can be used for multi-modal classification. There are also described various RBMs, that are suitable for different kinds of input data. A design and implementation of multimodal RBM is described together with results of preliminary experiments.

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Fredriksson, 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.

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Denna studie introducerar restricted Boltzmann machines (RBMs) som rekommendationsmodell i kontexten av riskkapital. Ett nätverk av relationer används som proxy för att modellera investerares bolagspreferenser. Studiens huvudfokus är att undersöka hur RBMs kan implementeras för ett dataset bestående av relationer mellan personer och bolag, samt att undersöka om modellen går att förbättra genom att tillföra av ytterligare information. Nätverket skapas från styrelsesammansättningar för svenska bolag. För nätverket implementeras RBMs både med och utan den extra informationen om bolagens ursprungs

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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.

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In this thesis we asses the consistency and convexity of the parameter inference in Boltzmann machine learning algorithms based on gradient ascent on the likelihood surface. We do this by rst developing standard tools for generating equillibrium data drawn from a Boltzmann distribution, as well as analytically exact algorithms for inferring the parameters of restricted and semi-restricted Boltzmann machine architctures. After testing, and showing, the functionality of our algorithms, we assess how dierent network properties eect the inferrence quality of restricted Boltzmann machines. Subseque

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Tubiana, Jérôme. "Restricted Boltzmann machines : from compositional representations to protein sequence analysis." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE039/document.

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Les Machines de Boltzmann restreintes (RBM) sont des modèles graphiques capables d’apprendre simultanément une distribution de probabilité et une représentation des données. Malgré leur architecture relativement simple, les RBM peuvent reproduire très fidèlement des données complexes telles que la base de données de chiffres écrits à la main MNIST. Il a par ailleurs été montré empiriquement qu’elles peuvent produire des représentations compositionnelles des données, i.e. qui décomposent les configurations en leurs différentes parties constitutives. Cependant, toutes les variantes de ce modèle

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Spiliopoulou, Athina. "Probabilistic models for melodic sequences." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8876.

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Structure is one of the fundamentals of music, yet the complexity arising from the vast number of possible variations of musical elements such as rhythm, melody, harmony, key, texture and form, along with their combinations, makes music modelling a particularly challenging task for machine learning. The research presented in this thesis focuses on the problem of learning a generative model for melody directly from musical sequences belonging to the same genre. Our goal is to develop probabilistic models that can automatically capture the complex statistical dependencies evident in music withou

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de, 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.

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Restricted Boltzmann Machines (RBMs) and autoencoders have been used - in several variants - for similar tasks, such as reducing dimensionality or extracting features from signals. Even though their structures are quite similar, they rely on different training theories. Lately, they have been largely used as building blocks in deep learning architectures that are called deep belief networks (instead of stacked RBMs) and stacked autoencoders. In light of this, the student has worked on this thesis with the aim to understand the extent of the similarities and the overall pros and cons of using e

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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.

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Dahlin, 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.

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User behavior analysis in web applications is currently mainly performed by analysis of statistical measurements based on user interactions or by creation of personas to better understand users. Both of these methods give great insights in how the users utilize a web site, but do not give any additional information about what they are actually doing. This thesis attempts to use eye tracking data for analysis of user activities in web applications. Eye tracking data has been recorded, labeled and analyzed for 25 test participants. No data source except eye tracking data has been used and two di

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Jin, 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.

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Dupuy, Nathalie. "Neurocomputational model for learning, memory consolidation and schemas." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33144.

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This thesis investigates how through experience the brain acquires and stores memories, and uses these to extract and modify knowledge. This question is being studied by both computational and experimental neuroscientists as it is of relevance for neuroscience, but also for artificial systems that need to develop knowledge about the world from limited, sequential data. It is widely assumed that new memories are initially stored in the hippocampus, and later are slowly reorganised into distributed cortical networks that represent knowledge. This memory reorganisation is called systems consolida

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Côté, Marc-Alexandre. "Réseaux de neurones génératifs avec structure." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10489.

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Cette thèse porte sur les modèles génératifs en apprentissage automatique. Deux nouveaux modèles basés sur les réseaux de neurones y sont proposés. Le premier modèle possède une représentation interne où une certaine structure a été imposée afin d’ordonner les caractéristiques apprises. Le deuxième modèle parvient à exploiter la structure topologique des données observées, et d’en tenir compte lors de la phase générative. Cette thèse présente également une des premières applications de l’apprentissage automatique au problème de la tractographie du cerveau. Pour ce faire, un réseau de neuron

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Schneider, C. "Using unsupervised machine learning for fault identification in virtual machines." Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.

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Self-healing systems promise operating cost reductions in large-scale computing environments through the automated detection of, and recovery from, faults. However, at present there appears to be little known empirical evidence comparing the different approaches, or demonstrations that such implementations reduce costs. This thesis compares previous and current self-healing approaches before demonstrating a new, unsupervised approach that combines artificial neural networks with performance tests to perform fault identification in an automated fashion, i.e. the correct and accurate determinati

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Pasa, Luca. "Linear Models and Deep Learning: Learning in Sequential Domains." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3425865.

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With the diffusion of cheap sensors, sensor-equipped devices (e.g., drones), and sensor networks (such as Internet of Things), as well as the development of inexpensive human-machine interaction interfaces, the ability to quickly and effectively process sequential data is becoming more and more important. There are many tasks that may benefit from advancement in this field, ranging from monitoring and classification of human behavior to prediction of future events. Most of the above tasks require pattern recognition and machine learning capabilities. There are many approaches that have been

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Habrnál, Matěj. "Hluboké neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236132.

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The thesis addresses the topic of Deep Neural Networks, in particular the methods regar- ding the field of Deep Learning, which is used to initialize the weight and learning process s itself within Deep Neural Networks. The focus is also put to the basic theory of the classical Neural Networks, which is important to comprehensive understanding of the issue. The aim of this work is to determine the optimal set of optional parameters of the algori- thms on various complexity levels of image recognition tasks through experimenting with created application applying Deep Neural Networks. Furthermor

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da, Costa Joel. "Online Non-linear Prediction of Financial Time Series Patterns." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32221.

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We consider a mechanistic non-linear machine learning approach to learning signals in financial time series data. A modularised and decoupled algorithm framework is established and is proven on daily sampled closing time-series data for JSE equity markets. The input patterns are based on input data vectors of data windows preprocessed into a sequence of daily, weekly and monthly or quarterly sampled feature measurement changes (log feature fluctuations). The data processing is split into a batch processed step where features are learnt using a Stacked AutoEncoder (SAE) via unsupervised learnin

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Yogeswaran, 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.

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Advances in unsupervised learning and deep neural networks have led to increased performance in a number of domains, and to the ability to draw strong comparisons between the biological method of self-organization conducted by the brain and computational mechanisms. This thesis aims to use real-world data to tackle two areas in the domain of computer vision which have biological equivalents: feature detection and motion tracking. The aforementioned advances have allowed efficient learning of feature representations directly from large sets of unlabeled data instead of using traditional handcr

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Hubený, 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.

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This work is focused on objects and scenes recognition using machine learning and computer vision tools. Before the solution of this problem has been studied basic phases of the machine learning concept and statistical models with accent on their division into discriminative and generative method. Further, the Bag-of-words method and its modification have been investigated and described. In the practical part of this work, the implementation of the Bag-of-words method with the SVM classifier was created in the Matlab environment and the model was tested on various sets of publicly available im

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Tsai, 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.

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博士<br>國立交通大學<br>電子研究所<br>105<br>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

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Pandey, Gaurav. "Deep Learning with Minimal Supervision." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4315.

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Abstract In recent years, deep neural networks have achieved extraordinary performance on supervised learning tasks. Convolutional neural networks (CNN) have vastly improved the state of the art for most computer vision tasks including object recognition and segmentation. However, their success relies on the presence of a large amount of labeled data. In contrast, relatively fewer work has been done in deep learning to handle scenarios when access to ground truth is limited, partial or completely absent. In this thesis, we propose models to handle challenging problems with limited labeled inf

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Anderson, David John. "Automatic speech feature extraction using a convolutional restricted boltzmann machine." Thesis, 2017. https://hdl.handle.net/10539/26165.

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A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfillment of the requirements for the degree of Master of Science 2017<br>Restricted 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 archi

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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.

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碩士<br>國立清華大學<br>電機工程學系<br>102<br>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

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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.

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Upadhya, Vidyadhar. "Efficient Algorithms for Learning Restricted Boltzmann Machines." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4840.

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The probabilistic generative models learn useful features from unlabeled data which can be used for subsequent problem-specific tasks, such as classification, regression or information retrieval. The RBM is one such important energy based probabilistic generative model. RBMs are also the building blocks for several deep generative models. It is difficult to train and evaluate RBMs mainly because the normalizing constant (known as the partition function) for the distribution that they represent is computationally hard to evaluate. Therefore, various approximate methods (based noisy gradient

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Tai, 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.

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碩士<br>國立臺北科技大學<br>電腦與通訊研究所<br>100<br>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

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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.

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碩士<br>國立清華大學<br>電機工程學系<br>102<br>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

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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.

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碩士<br>國立清華大學<br>電機工程學系<br>105<br>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 ide

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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.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>104<br>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 pr

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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.

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Kai-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.

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KOUTOU, 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.

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碩士<br>國立清華大學<br>資訊系統與應用研究所<br>106<br>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 ext

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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.

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碩士<br>南臺科技大學<br>電機工程系<br>106<br>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 im

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Susskind, Joshua Matthew. "Interpreting Faces with Neurally Inspired Generative Models." Thesis, 2011. http://hdl.handle.net/1807/29884.

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Becoming a face expert takes years of learning and development. Many research programs are devoted to studying face perception, particularly given its prerequisite role in social interaction, yet its fundamental neural operations are poorly understood. One reason is that there are many possible explanations for a change in facial appearance, such as lighting, expression, or identity. Despite general agreement that the brain extracts multiple layers of feature detectors arranged into hierarchies to interpret causes of sensory information, very little work has been done to develop computational

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Yu, 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.

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碩士<br>國立清華大學<br>電機工程學系所<br>106<br>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

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"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.

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abstract: Animals learn to choose a proper action among alternatives according to the circumstance. Through trial-and-error, animals improve their odds by making correct association between their behavioral choices and external stimuli. While there has been an extensive literature on the theory of learning, it is still unclear how individual neurons and a neural network adapt as learning progresses. In this dissertation, single units in the medial and lateral agranular (AGm and AGl) cortices were recorded as rats learned a directional choice task. The task required the rat to make a left/right

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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.

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Lajoie, Isabelle. "Apprentissage de représentations sur-complètes par entraînement d’auto-encodeurs." Thèse, 2009. http://hdl.handle.net/1866/3768.

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Les avancés dans le domaine de l’intelligence artificielle, permettent à des systèmes informatiques de résoudre des tâches de plus en plus complexes liées par exemple à la vision, à la compréhension de signaux sonores ou au traitement de la langue. Parmi les modèles existants, on retrouve les Réseaux de Neurones Artificiels (RNA), dont la popularité a fait un grand bond en avant avec la découverte de Hinton et al. [22], soit l’utilisation de Machines de Boltzmann Restreintes (RBM) pour un pré-entraînement non-supervisé couche après couche, facilitant grandement l’entraînement supervisé du rése

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Taylor, Graham William. "Composable, Distributed-state Models for High-dimensional Time Series." Thesis, 2009. http://hdl.handle.net/1807/19238.

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Abstract:

In this thesis we develop a class of nonlinear generative models for high-dimensional time series. The first key property of these models is their distributed, or "componential" latent state, which is characterized by binary stochastic variables which interact to explain the data. The second key property is the use of an undirected graphical model to represent the relationship between latent state (features) and observations. The final key property is composability: the proposed class of models can form the building blocks of deep networks by successively training each model on the features ex

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