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Dissertations / Theses on the topic 'Deep Belief Network'

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Published: 4 June 2021

Last updated: 25 July 2025

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1

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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Larsson, Marcus, and Christoffer Möckelind. "The effects of Deep Belief Network pre-training of a Multilayered perceptron under varied labeled data conditions." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187374.

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Sometimes finding labeled data for machine learning tasks is difficult. This is a problem for purely supervised models like the Multilayered perceptron(MLP). A Discriminative Deep Belief Network(DDBN) is a semi-supervised model that is able to use both labeled and unlabeled data. This research aimed to move towards a rule of thumb of when it is beneficial to use a DDBN instead of an MLP, given the proportions of labeled and unlabeled data. Several trials with different amount of labels, from the MNIST and Rectangles-Images datasets, were conducted to compare the two models. It was found that f

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Tong, Zheng. "Evidential deep neural network in the framework of Dempster-Shafer theory." Thesis, Compiègne, 2022. http://www.theses.fr/2022COMP2661.

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Les réseaux de neurones profonds (DNN) ont obtenu un succès remarquable sur de nombreuses applications du monde réel (par exemple, la reconnaissance de formes et la segmentation sémantique), mais sont toujours confrontés au problème de la gestion de l'incertitude. La théorie de Dempster-Shafer (DST) fournit un cadre bien fondé et élégant pour représenter et raisonner avec des informations incertaines. Dans cette thèse, nous avons proposé un nouveau framework utilisant DST et DNNs pour résoudre les problèmes d'incertitude. Dans le cadre proposé, nous hybridons d'abord DST et DNN en branchant un

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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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Nassar, Alaa S. N. "A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/16917.

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Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal

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Nguyen, Tien Dung. "Multimodal emotion recognition using deep learning techniques." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/180753/1/Tien%20Dung_Nguyen_Thesis.pdf.

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This thesis investigates the use of deep learning techniques to address the problem of machine understanding of human affective behaviour and improve the accuracy of both unimodal and multimodal human emotion recognition. The objective was to explore how best to configure deep learning networks to capture individually and jointly, the key features contributing to human emotions from three modalities (speech, face, and bodily movements) to accurately classify the expressed human emotion. The outcome of the research should be useful for several applications including the design of social robots.

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Faulkner, Ryan. "Dyna learning with deep belief networks." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97177.

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The objective of reinforcement learning is to find "good" actions in an environment where feedback is provided through a numerical reward, and the current state (i.e. sensory input) is assumed to be available at each time step. The notion of "good" is defined as maximizing the expected cumulative returns over time. Sometimes it is useful to construct models of the environment to aid in solving the problem. We investigate Dyna-style reinforcement learning, a powerful approach for problems where not much real data is available. The main idea is to supplement real trajectories with simulated o

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Josefsson, Alexandra. "Modeling an Embedded Climate System Using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290676.

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Recent advancements in processing power, storage capabilities, and availability of data, has led to improvements in many applications through the use of machine learning. Using machine learning in control systems was first suggested in the 1990s, but is more recently being implemented. In this thesis, an embedded climate system, which is a type of control system, will be looked at. The ways in which machine learning can be used to replicate portions of the climate system is looked at. Deep Belief Networks are the machine learning models of choice. Firstly, the functionality of a PID controller

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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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Imbulgoda, Liyangahawatte Gihan Janith Mendis. "Hardware Implementation and Applications of Deep Belief Networks." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1476707730643462.

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Liu, Ye. "Application of Convolutional Deep Belief Networks to Domain Adaptation." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397728737.

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12

Hasasneh, Ahmad. "Robot semantic place recognition based on deep belief networks and a direct use of tiny images." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00960289.

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Usually, human beings are able to quickly distinguish between different places, solely from their visual appearance. This is due to the fact that they can organize their space as composed of discrete units. These units, called ''semantic places'', are characterized by their spatial extend and their functional unity. Such a semantic category can thus be used as contextual information which fosters object detection and recognition. Recent works in semantic place recognition seek to endow the robot with similar capabilities. Contrary to classical localization and mapping works, this problem is us

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Bosello, Michael. "Integrating BDI and Reinforcement Learning: the Case Study of Autonomous Driving." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21467/.

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Recent breakthroughs in machine learning are paving the way to the vision of software 2.0 era, which foresees the replacement of traditional software development with such techniques for many applications. In the context of agent-oriented programming, we believe that mixing together cognitive architectures like the BDI one and learning techniques could trigger new interesting scenarios. In that view, our previous work presents Jason-RL, a framework that integrates BDI agents and Reinforcement Learning (RL) more deeply than what has been already proposed so far in the literature. The framework

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Kaabi, Rabeb. "Apprentissage profond et traitement d'images pour la détection de fumée." Electronic Thesis or Diss., Toulon, 2020. http://www.theses.fr/2020TOUL0017.

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Cette thèse aborde le problème de la détection des feux de forêt par des outils de traitement d’images et apprentissage machine. Un incendie de forêt est un feu qui se propage sur une étendue boisée. Il peut être d'origine naturelle (dû à la foudre ou à une éruption volcanique) ou humaine. Dans le monde entier, l’impact des feux de forêts sur de nombreux aspects de notre vie quotidienne se fait de plus en plus apparente sur l’écosystème entier. De nombreuses méthodes ont montré l’efficacité pour la détection des incendies de forêt. L’originalité du présent travail réside dans la détection préc

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Jost, Ingo. "Aplicação de Deep Learning em dados refinados para Mineração de Opiniões." Universidade do Vale do Rio dos Sinos, 2015. http://www.repositorio.jesuita.org.br/handle/UNISINOS/3841.

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Submitted by Maicon Juliano Schmidt (maicons) on 2015-06-12T19:13:14Z No. of bitstreams: 1 Ingo Jost.pdf: 1217467 bytes, checksum: bf67cd6724b1cd182a12a3cd7b5af1eb (MD5)<br>Made available in DSpace on 2015-06-12T19:13:14Z (GMT). No. of bitstreams: 1 Ingo Jost.pdf: 1217467 bytes, checksum: bf67cd6724b1cd182a12a3cd7b5af1eb (MD5) Previous issue date: 2015-02-26<br>CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Deep Learning é uma sub-área de Aprendizado de Máquina que tem obtido resultados sa- tisfatórios em várias áreas de aplicação, implementada por diferentes algo

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Berry, Jeffrey James. "Machine Learning Methods for Articulatory Data." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/223348.

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Humans make use of more than just the audio signal to perceive speech. Behavioral and neurological research has shown that a person's knowledge of how speech is produced influences what is perceived. With methods for collecting articulatory data becoming more ubiquitous, methods for extracting useful information are needed to make this data useful to speech scientists, and for speech technology applications. This dissertation presents feature extraction methods for ultrasound images of the tongue and for data collected with an Electro-Magnetic Articulograph (EMA). The usefulness of these featu

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Olsson, Sebastian. "Automated sleep scoring using unsupervised learning of meta-features." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189234.

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Sleep is an important part of life as it affects the performance of one's activities during all awake hours. The study of sleep and wakefulness is therefore of great interest, particularly to the clinical and medical fields where sleep disorders are diagnosed. When studying sleep, it is common to talk about different types, or stages, of sleep. A common task in sleep research is to determine the sleep stage of the sleeping subject as a function of time. This process is known as sleep stage scoring. In this study, I seek to determine whether there is any benefit to using unsupervised feature le

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Sadli, Rahmad. "Étude et développement d'un dispositif routier d'anticollision basé sur un radar ultra large bande pour la détection et l'identification notamment des usagers vulnérables." Thesis, Valenciennes, 2019. http://www.theses.fr/2019VALE0005.

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Dans ce travail de thèse, nous présentons nos travaux qui portent sur l’identification des cibles en général par un radar Ultra-Large Bande (ULB) et en particulier l’identification des cibles dont la surface équivalente radar est faible telles que les piétons et les cyclistes. Ce travail se décompose en deux parties principales, la détection et la reconnaissance. Dans la première approche du processus de détection, nous avons proposé et étudié un détecteur de radar ULB robuste qui fonctionne avec des données radar 1-D (A-scan) à une dimension. Il exploite la combinaison des statistiques d’ordr

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Lam, Michael. "Retinotopic Preservation in Deep Belief Network Visual Learning." Thesis, 2011. http://hdl.handle.net/10012/5894.

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One of the foremost characteristics of the mammalian visual system is the retinotopic mapping observed in the low-level visual processing centres; the spatial pattern of activation in the lateral geniculate nucleus and primary visual cortex corresponds topologically to the pattern of light falling on the retina. Various vision systems have been developed that take advantage of structured input such as retinotopy, however these systems are often not biologically plausible. Using a parsimonious approach for implementing retinotopy, one that is based on the biology of our visual pathway, we run s

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HSIEH, CHEN-EN, and 謝承恩. "Hardware Implementation of Deep Belief Network with Stochastic Computing." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/tjsjw4.

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碩士<br>國立高雄科技大學<br>電子工程系<br>107<br>The deep belief network (DBN) is a classic and representative neural network designed to solve classification problems. Stochastic computing (SC) is a highly efficient and attractive paradigm with low-cost hardware, the computation operation can be implemented by simple logic gates. The range of the conventional SC in the bipolar format is limited in the interval of [-1, 1], while the integral stochastic computing (ISC) expands the range to [-m, m], where m is the number of input streams. The new integral stochastic computing (NISC) has recently been introduc

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CHU, JUNG-HUI, and 朱容慧. "Applying Deep Belief Network to Forecast Air Pollution Concentration." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/67wf7m.

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碩士<br>龍華科技大學<br>資訊管理系碩士班<br>106<br>The issue of air pollution is more and more important because influence of air pollution is also increasing in the world. The environment is affected by air pollution which makes the plant is slowly grow, genetic mutations, or diseases in humans. This study proposed a suitable prediction models for air pollutants in various regions, and the prediction model can obtain better performance. When anomalies are predicted, early warning can be provided to increase the time for prevention. This study collected data on air pollutants from four monitoring stations whi

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Ruiz, Vito Manuel. "Adaptation in a deep network." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-05-3156.

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Though adaptational effects are found throughout the visual system, the underlying mechanisms and benefits of this phenomenon are not yet known. In this work, the visual system is modeled as a Deep Belief Network, with a novel “post-training” paradigm (i.e. training the network further on certain stimuli) used to simulate adaptation in vivo. An optional sparse variant of the DBN is used to help bring about meaningful and biologically relevant receptive fields, and to examine the effects of sparsification on adaptation in their own right. While results are inconclusive, there is some evidence o

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Chen, Yi-Ting, and 陳奕廷. "Application of Deep Belief Network on Binaural Speech Separation and Dereverberation." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/34100240484476159416.

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碩士<br>國立交通大學<br>電信工程研究所<br>103<br>Binaural speech separation and de-reverberation are popular research topics and we have developed an unsupervised clustering method for these purposes. In this thesis, we adopt a supervised classification method for binaural speech separation and de-reverberation using the ideal binary mask (IBM) as the training target and a deep belief network (DBN) as the classifier. We extract the interaural time difference (ITD) and the interaural level difference (ILD) of each T-F unit as the binaural features. To boost the performance of de-reverberation, the interaural

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Lin, Yu-Jie, and 林鈺傑. "The Use of Deep Belief Network Technology to Predict the Stock Price Changes." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/tkqgpw.

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碩士<br>元智大學<br>資訊工程學系<br>106<br>Compared to the past, people pay more and more attention to investment, how to make better use of limited funds to become the important of thinking. Certificate of exchange in the Taiwan 104 years the latest statistics of the cumulative number of shares of more than 17 million households, we can see that stock trading in Taiwan has become an indispensable investment pipeline. Stock price data provide a successful example in the stock forecast market where artificial intelligence (AI) techniques such as the Neuron Network have been widely used to predict stock pri

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Yu-PeiHuang and 黃裕培. "Devising a Model to Predict Financial Distress Based on the Deep Belief Network Algorithm." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/27zz3p.

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碩士<br>國立成功大學<br>高階管理碩士在職專班(EMBA)<br>105<br>The use of new artificial intelligent (AI) techniques, such as machine learning (ML) in the accounting domains, have unleashed great potential for researchers to improve accounting information systems (AIS). An automatic AIS reports financial statements from the supporting documents. The basic four financial statements provide information about the results of operations and financial position of an enterprise. As a result, those financial statements could be used to establish a diagnosis model for financial distress prediction (FDP). This study propos

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Wheng, Ko-Cheng, and 翁恪誠. "Multi-Task Learning based Deep Belief Network for Speech Emotion Recognition using Spectro-Temporal Modulations." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/dbfe9d.

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碩士<br>國立交通大學<br>電信工程研究所<br>103<br>Speech emotion recognition is a popular research topic from the last decade. Meanwhile, since the revival of deep learning in 2007, it has been adopted in various research fields. In this thesis, we use a deep belief network (DBN) as the classifier and examine its performance in detecting emotion states of noisy speech signals using rate-scale features (RS features) extracted from an auditory model. The noisy speech is derived by adding white and babble noises to clean utterances from the Berlin Emotional Speech database under various SNR levels. Afterward, th

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Brocardo, Marcelo Luiz. "Continuous Authentication using Stylometry." Thesis, 2015. http://hdl.handle.net/1828/6098.

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Static authentication, where user identity is checked once at login time, can be circumvented no matter how strong the authentication mechanism is. Through attacks such as man-in-the-middle and its variants, an authenticated session can be hijacked later after the initial login process has been completed. In the last decade, continuous authentication (CA) using biometrics has emerged as a possible remedy against session hijacking. CA consists of testing the authenticity of the user repeatedly throughout the authenticated session as data becomes available. CA is expected to be carried out unobt

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Chen, Ying-Tsen, and 陳映岑. "Applying the Method of Deep Belief Network Pre-trained by Restricted Boltzmann Machines on High Confused Mandarin Vowel Recognition." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8pukp3.

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碩士<br>國立中興大學<br>統計學研究所<br>106<br>This thesis mainly uses deep belief network (DBN) pre-trained by restricted Boltzmann machine (RBM) to recognize high confused mandarin vowels such as ㄢ, ㄤ>, ㄛ , ㄨㄛ>, ㄥ, ㄣ>, etc. First, we would record the phonetic data of 20 speakers, and then perform a series of pre-processing such as digital sampling, endpoint detection, frame cutting, and windowing. Then take Mel-frequency cepstral coefficients (MFCC) as the features of the phonetic data, and use these features as the input to train the model. Different from multilayer perceptron (MLP) which uses random ini

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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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Al-Waisy, Alaa S., Rami S. R. Qahwaji, Stanley S. Ipson, and Shumoos Al-Fahdawi. "A multimodal deep learning framework using local feature representations for face recognition." 2017. http://hdl.handle.net/10454/13122.

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Yes<br>The most recent face recognition systems are mainly dependent on feature representations obtained using either local handcrafted-descriptors, such as local binary patterns (LBP), or use a deep learning approach, such as deep belief network (DBN). However, the former usually suffers from the wide variations in face images, while the latter usually discards the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the DBN is proposed to addre

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Cummer, Jason. "Methodology and Techniques for Building Modular Brain-Computer Interfaces." Thesis, 2014. http://hdl.handle.net/1828/5837.

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Commodity brain-computer interfaces (BCI) are beginning to accompany everything from toys and games to sophisticated health care devices. These contemporary interfaces allow for varying levels of interaction with a computer. Not surprisingly, the more intimately BCIs are integrated into the nervous system, the better the control a user can exert on a system. At one end of the spectrum, implanted systems can enable an individual with full body paralysis to utilize a robot arm and hold hands with their loved ones [28, 62]. On the other end of the spectrum, the untapped potential of commodit

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Lee, Changhsun, and 李昌訓. "Face recognition using Deep Belief Networks." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/08946004798906414934.

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碩士<br>國立中正大學<br>電機工程研究所<br>100<br>Face recognition is an important field of research because it is important for many applications in our daily life such as card identification or key password identification. It is also an important part of the software for service robots, since face recognition is also an important function on robots. In this paper, we use the OpenCV to do face detection and feature point extraction, and then we input the feature point data into the Deep Belief Networks. Layer-by-layer the weights are learned to make out faces from the networks to implement the purpose of rea

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Lu, LiWei, and 盧立偉. "Preparing Deep Belief Networks for Practical Tasks." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/68415284452640040030.

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碩士<br>國立中正大學<br>電機工程研究所<br>100<br>Deep Belief Networks (DBNs) is a probabilistic generative models composed of multiple layers of stochastic, latent variables. multiple layers of stochastic, latent variables. The network can learn many layers of features on various type of data such as binary images, gray scaled images, color images and acoustic data. This paper further examined the ability of DBNs to interpret the binary representation of data. The performance is validated by learning given distributions such as normal distribution, Poisson distribution and random number generator. We have sh

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HSU, CHI, and 許霽. "Music Emotion Classification Using Deep Belief Networks." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/18411289638893719664.

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碩士<br>國立中正大學<br>資訊工程研究所<br>102<br>Deep learning which is capable of learning features from raw data is one of the most important methods in recent machine learning research with remarkable successes in many practical object recognition tasks, such as face recognition, traffic sign recognition, or even Dogs vs. Cats image recognition. It have been used by many winners in recent Kaggle Data Science competitions. In this thesis, we propose to study the effectives of features learned by deep learning for music data. We studied two unsupervised feature learning systems, Restricted Boltzmann Machine

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Weng, Ching Hua, and 翁景華. "Drowsy Driver Detection Systems with Deep Belief Networks." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/67918469401781687678.

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碩士<br>國立清華大學<br>資訊工程學系<br>103<br>Drowsy driver alert systems have been developed to reduce and prevent car accidents. Existing vision-based systems are usually restricted to using visual cues, and they usually depend on tedious parameter tuning or cannot work under general conditions. One additional crucial issue is the lack of public datasets that can be used to evaluate the performance of different methods. In this thesis, we develop two novel systems, i.e. a Component-wise Discretized Deep Belief Network (CDDBN) system and a novel Hierarchical Temporal Deep Belief Network (HTDBN) system, fo

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Wen, Chan-Wei, and 溫禪維. "Intonation Analysis in Standard Mandarin using Deep Belief Networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/q335aj.

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碩士<br>國立中正大學<br>資訊工程研究所<br>105<br>Deep belief network is a commonly used method in machine learning domain, such as image recognition, speech recognition, etc. With a little pre-processing, it can learn the features from the raw data excellently. In many languages around the world, Standard Mandarin is one of few tonal languages, different tones will make a speech differently. Therefore speech recognition in Standard Mandarin is highly difficult than other language for instance English. In other words, it means that the correctness of tonal recognition has a great influence on the correctness

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Hung, Fei-Fan, and 洪非凡. "Mandarin Chinese Phoneme Recognition Based on Deep Belief Networks." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/66279718173113970528.

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碩士<br>國立交通大學<br>電子工程學系電子研究所<br>103<br>In this thesis, we apply a deep architecture, Deep Belief Networks (DBNs), to perform Mandarin Chinese phoneme recognition. There are four main tones in Mandarin Chinese and every Chinese character is articulated as an individual syllable with distinct tones. Since a syllable pronounced in different tones may result in distinctive meanings, tones play an essential role in Mandarin Chinese phoneme recognition. This property is quite different from English and some other non-tonal languages. In this thesis, we recognize Mandarin Chinese by utilizing DBNs (D

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HUANG, GUO-YU, and 黃國瑜. "A Study on Deep Belief Networks in Rainfall Forecasting." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ye8589.

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碩士<br>國立暨南國際大學<br>資訊管理學系<br>107<br>In recent years with the development of technology, public daily life is surrounded by technology applications, indicating that people can collect all sorts of data without efforts. In which atmospheric data is highly related to public life, no matter for agriculture, for the fishery or for the service industry, good forecasts rely on accurate atmospheric data. There are many studies and concerned authorities try to find an effective way to forecast with accuracy. In this study, we build hourly rainfall forecast models with the 2017 atmospheric factors datase

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Chen, Kuan-Ting, and 陳冠廷. "Warping of Human Face View using Convolutional Deep Belief Networks." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/80694902748248773004.

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碩士<br>國立交通大學<br>電子工程學系電子研究所<br>103<br>In this thesis, we aim at finding a better way of representing and connecting related human face images using the learning approach in convolutional deep belief networks (DBN). Since images are connecting with corresponding representations, it is possible for the convolutional DBN to infer a human face image with a view angle by a given image with the same human face from another view angle. The proposed methods are shown to work well due to the fact that the features detected on an image of an object in different movements are highly correlated. If patte

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Tsai, Chi-Hung, and 蔡志宏. "Multi-Feature Based 3D Face Recognition Using Deep Belief Networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/47116964262274485939.

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碩士<br>國立臺北大學<br>多媒體與網路科技產業碩士專班<br>105<br>The success rate for modern face recognition systems has reached around 99\% for frontal face recognition. But for other views the success rate is much lower and hence poses serious challenges for real time applications of face recognition algorithms. It has been shown that general 2D characteristics are insufficient to handle variations in facial positions and other illumination or environmental changes. This work focuses on the use of three-dimensional features of the Point Cloud to replace two-dimensional features so as to obtain reliable recognition

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Vojt, Ján. "Deep neural networks and their implementation." Master's thesis, 2016. http://www.nusl.cz/ntk/nusl-345228.

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Deep neural networks represent an effective and universal model capable of solving a wide variety of tasks. This thesis is focused on three different types of deep neural networks - the multilayer perceptron, the convolutional neural network, and the deep belief network. All of the discussed network models are implemented on parallel hardware, and thoroughly tested for various choices of the network architecture and its parameters. The implemented system is accompanied by a detailed documentation of the architectural decisions and proposed optimizations. The efficiency of the implemented frame

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Jhuang, Dong-Han, and 莊東翰. "Face Verification with Three-Dimensional Point Cloud by Using Deep Belief Networks." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/45788712097104409134.

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碩士<br>國立臺北大學<br>資訊工程學系<br>103<br>Developing face recognition systems has been a challenge for decades. The variation in illumination and head pose may decrease the accuracy of two-dimensional face recognition. With the invention of a depth map sensor, more three-dimensional volume data can be processed to mitigate the problem associated with face verification. This paper describes our three-dimensional face verification approach in three phases. First, point cloud library is applied to estimate normal vectors and principal curvatures of every point on a human face point cloud acquired from thr

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LAI, MENG-NAN, and 賴孟南. "The Performance Analysis of Deep Belief Networks: A Case Study of PM2.5." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/rax6j4.

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碩士<br>國立暨南國際大學<br>資訊管理學系<br>107<br>This study mainly discusses whether data preprocessing is helpful for training of Deep Belief Networks(DBN). Using air pollution data, we first lower the noise by preprocessing and fill the missing values. Then we use logarithm(LOG), verification, stepwise regression, wavelet analysis to change the data structure, or eliminate some of the independent variables to lower the noises. We use one of metaheuristic algorithms, namely Genetic Algorithm(GA), to help searching parameters, decide network structure to minimize human involve, and to prevent local minimum

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Chu, Joseph Lin. "Using Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks for Partially Occluded Object Recognition." Thesis, 2014. http://spectrum.library.concordia.ca/978484/1/Chu_MCompSc_S2014.pdf.

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Artificial neural networks have been widely used for machine learning tasks such as object recognition. Recent developments have made use of biologically inspired architectures, such as the Convolutional Neural Network, and the Deep Belief Network. A theoretical method for estimating the optimal number of feature maps for a Convolutional Neural Network maps using the dimensions of the receptive field or convolutional kernel is proposed. Empirical experiments are performed that show that the method works to an extent for extremely small receptive fields, but doesn't generalize as clearly to

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Švaralová, Monika. "DRESS & GO: Deep belief networks and Rule Extraction Supported by Simple Genetic Optimization." Master's thesis, 2018. http://www.nusl.cz/ntk/nusl-383249.

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Recent developments in social media and web technologies offer new opportunities to access, analyze and process ever-increasing amounts of fashion-related data. In the appealing context of design and fashion, our main goal is to automatically suggest fashionable outfits based on the preferences extracted from real-world data provided either by individual users or gathered from the internet. In our case, the clothing items have the form of 2D-images. Especially for visual data processing tasks, recent models of deep neural networks are known to surpass human performance. This fact inspired us t

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Liu, Yu-ting, and 劉郁廷. "Recognition of Guitar Playing Techniques with Deep Belief Networks based on Spectral-Temporal Receptive Fields." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/88540315134745849099.

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碩士<br>國立中央大學<br>通訊工程學系<br>103<br>Guitar is a very common instrument which has been widely used in popular music, rock, ballad, etc. Different guitar playing technique can perform various vocal, express different emotion, then play the wonderful music. Some of guitar playing techniques has only tiny difference. To recognize it is a big challenge. This paper proposed a guitar playing technique recognition system including a novel STRF based feature extraction algorithm and a novel deep learning model called HCDBN. In experiments, the proposed system improves 11.74% recognition rate than baseline

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Golovizin, Andrey. "Deep neural networks and their application for image data processing." Master's thesis, 2016. http://www.nusl.cz/ntk/nusl-346753.

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In the area of image recognition, the so-called deep neural networks belong to the most promising models these days. They often achieve considerably better results than traditional techniques even without the necessity of any excessive task-oriented preprocessing. This thesis is devoted to the study and analysis of three basic variants of deep neural networks-namely the neocognitron, convolutional neural networks, and deep belief networks. Based on extensive testing of the described models on the standard task of handwritten digit recognition, the convolutional neural networks seem to be most

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