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Relevant bibliographies by topics / Binary neural networks / Dissertations / Theses

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Dissertations / Theses on the topic 'Binary neural networks'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Braga, AntoÌ‚nio de PaÌdua. "Design models for recursive binary neural networks." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336442.

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2

Redkar, Shrutika. "Deep Learning Binary Neural Network on an FPGA." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/407.

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In recent years, deep neural networks have attracted lots of attentions in the field of computer vision and artificial intelligence. Convolutional neural network exploits spatial correlations in an input image by performing convolution operations in local receptive fields. When compared with fully connected neural networks, convolutional neural networks have fewer weights and are faster to train. Many research works have been conducted to further reduce computational complexity and memory requirements of convolutional neural networks, to make it applicable to low-power embedded applications. T

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3

Kennedy, John V. "The design of a scalable and application independent platform for binary neural networks." Thesis, University of York, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323503.

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4

Li, Guo. "Neural network for optimization of binary computer-generated hologram with printing model /." Online version of thesis, 1995. http://hdl.handle.net/1850/12234.

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5

Medvedieva, S. O., I. V. Bogach, V. A. Kovenko, С. О. Медведєва, І. В. Богач, and В. А. Ковенко. "Neural networks in Machine learning." Thesis, ВНТУ, 2019. http://ir.lib.vntu.edu.ua//handle/123456789/24788.

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В статті розглянуті основи роботи з нейронними мережами, особливу увагу приділено моделі мережі під назвою «перцептрон», запровадженої Френком Розенблаттом. До того ж було розкрито тему найпоширеніших мов програмування, що дозволяють втілити нейронні мережі у життя, шляхом створення програмного забезпечення, пов`язаного з ними.<br>The paper covers the basic principles of Neural Networks’ work. Special attention is paid to Frank Rosenblatt’s model of the network called “perceptron”. In addition, the article touches upon the main programming languages used to write software for Neural Networks

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Simons, Taylor Scott. "High-Speed Image Classification for Resource-Limited Systems Using Binary Values." BYU ScholarsArchive, 2021. https://scholarsarchive.byu.edu/etd/9097.

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Image classification is a memory- and compute-intensive task. It is difficult to implement high-speed image classification algorithms on resource-limited systems like FPGAs and embedded computers. Most image classification algorithms require many fixed- and/or floating-point operations and values. In this work, we explore the use of binary values to reduce the memory and compute requirements of image classification algorithms. Our objective was to implement these algorithms on resource-limited systems while maintaining comparable accuracy and high speeds. By implementing high-speed image class

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Mealey, Thomas C. "Binary Recurrent Unit: Using FPGA Hardware to Accelerate Inference in Long Short-Term Memory Neural Networks." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1524402925375566.

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8

Holesovsky, Ondrej. "Compact ConvNets with Ternary Weights and Binary Activations." Thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-216389.

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Compact architectures, ternary weights and binary activations are two methods suitable for making neural networks more efficient. We introduce a) a dithering binary activation which improves accuracy of ternary weight networks with binary activations by randomizing quantization error, and b) a method of implementing ternary weight networks with binary activations using binary operations. Despite these new approaches, training a compact SqueezeNet architecture with ternary weights and full precision activations on ImageNet degrades classification accuracy significantly more than when training a

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9

Strandberg, Rickard, and Johan Låås. "A comparison between Neural networks, Lasso regularized Logistic regression, and Gradient boosted trees in modeling binary sales." Thesis, KTH, Optimeringslära och systemteori, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252556.

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The primary purpose of this thesis is to predict whether or not a customer will make a purchase from a specific item category. The historical data is provided by the Nordic online-based IT-retailer Dustin. The secondary purpose is to evaluate how well a fully connected feed forward neural network performs as compared to Lasso regularized logistic regression and gradient boosted trees (XGBoost) on this task. This thesis finds XGBoost to be superior to the two other methods in terms of prediction accuracy, as well as speed.<br>Det primära syftet med denna uppsats är att förutsäga huruvida en

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10

Bergtold, Jason Scott. "Advances in Applied Econometrics: Binary Discrete Choice Models, Artificial Neural Networks, and Asymmetries in the FAST Multistage Demand System." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/27266.

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The dissertation examines advancements in the methods and techniques used in the field of econometrics. These advancements include: (i) a re-examination of the underlying statistical foundations of statistical models with binary dependent variables. (ii) using feed-forward backpropagation artificial neural networks for modeling dichotomous choice processes, and (iii) the estimation of unconditional demand elasticities using the flexible multistage demand system with asymmetric partitions and fixed effects across time. The first paper re-examines the underlying statistical foundations of stati

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11

Sibanda, Wilbert. "Comparative study of neural networks and design of experiments to the classification of HIV status / Wilbert Sibanda." Thesis, North West University, 2013. http://hdl.handle.net/10394/13179.

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This research addresses the novel application of design of experiment, artificial neural net-works and logistic regression to study the effect of demographic characteristics on the risk of acquiring HIV infection among the antenatal clinic attendees in South Africa. The annual antenatal HIV survey is the only major national indicator for HIV prevalence in South Africa. This is a vital technique to understand the changes in the HIV epidemic over time. The annual antenatal clinic data contains the following demographic characteristics for each pregnant woman; age (herein called mother's age), pa

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Al-Shammaa, Mohammed. "Granular computing approach for intelligent classifier design." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13686.

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Granular computing facilitates dealing with information by providing a theoretical framework to deal with information as granules at different levels of granularity (different levels of specificity/abstraction). It aims to provide an abstract explainable description of the data by forming granules that represent the features or the underlying structure of corresponding subsets of the data. In this thesis, a granular computing approach to the design of intelligent classification systems is proposed. The proposed approach is employed for different classification systems to investigate its effici

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Kindbom, Hannes. "LSTM vs Random Forest for Binary Classification of Insurance Related Text." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252748.

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The field of natural language processing has received increased attention lately, but less focus is put on comparing models, which differ in complexity. This thesis compares Random Forest to LSTM, for the task of classifying a message as question or non-question. The comparison was done by training and optimizing the models on historic chat data from the Swedish insurance company Hedvig. Different types of word embedding were also tested, such as Word2vec and Bag of Words. The results demonstrated that LSTM achieved slightly higher scores than Random Forest, in terms of F1 and accuracy. The mo

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Gardner, Angelica. "Stronger Together? An Ensemble of CNNs for Deepfakes Detection." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-97643.

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Deepfakes technology is a face swap technique that enables anyone to replace faces in a video, with highly realistic results. Despite its usefulness, if used maliciously, this technique can have a significant impact on society, for instance, through the spreading of fake news or cyberbullying. This makes the ability of deepfakes detection a problem of utmost importance. In this paper, I tackle the problem of deepfakes detection by identifying deepfakes forgeries in video sequences. Inspired by the state-of-the-art, I study the ensembling of different machine learning solutions built on convolu

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Nguyen, Thanh Le Vi. "Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2020. https://ro.ecu.edu.au/theses/2359.

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In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication

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Singh, Gurpreet. "Statistical Modeling of Dynamic Risk in Security Systems." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273599.

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Big data has been used regularly in finance and business to build forecasting models. It is, however, a relatively new concept in the security industry. This study predicts technology related alarm codes that will sound in the coming 7 days at location $L$ by observing the past 7 days. Logistic regression and neural networks are applied to solve this problem. Due to the problem being of a multi-labeled nature logistic regression is applied in combination with binary relevance and classifier chains. The models are trained on data that has been labeled with two separate methods, the first method

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Le, Thu Anh. "An Exploration of the Word2vec Algorithm: Creating a Vector Representation of a Language Vocabulary that Encodes Meaning and Usage Patterns in the Vector Space Structure." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc849728/.

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This thesis is an exloration and exposition of a highly efficient shallow neural network algorithm called word2vec, which was developed by T. Mikolov et al. in order to create vector representations of a language vocabulary such that information about the meaning and usage of the vocabulary words is encoded in the vector space structure. Chapter 1 introduces natural language processing, vector representations of language vocabularies, and the word2vec algorithm. Chapter 2 reviews the basic mathematical theory of deterministic convex optimization. Chapter 3 provides background on some concepts

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Левчук, Святослав Богданович. "Інтелектуальна система мерчандайзингу. Детекція та розпізнавання асортименту". Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/23987.

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Магістерська дисертація: 126 с., 47 рис., 30 табл., 2 додатки, 31 джерело. Об'єктом дослідження є методи мерчандайзингу товарів в торгових точках. Предметом дослідження є методи класифікації товарів на полицях в торгових точках. Мета роботи – розробка інтелектуальної системи мерчендайзингу, яка дозволить зменшити використання людського ресурсу та максимально оптимізувати процес мерчендайзингу за рахунок автоматичного моніторингу наявності товару на полицях та розробка системи класифікації товарів як складової частини системи мерчиндайзингу для аналізу товарів на полиці відносно планограм

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Teixeira, Alex Fernandes Rocha. "Identificação de uma coluna de destilação de metanol-água através de modelos paramétricos e redes neurais artificiais." Universidade Federal de Alagoas, 2011. http://www.repositorio.ufal.br/handle/riufal/1195.

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This work presents a black box identification for a continuous methanol-water distillation column setting in open loop and closed loop response. Step changes and Pseudo-Random Binary Signal (PRBS) disturbance were used to excite the plant. The mathematical models candidates to identify were the Artificial Neural Networks (ANN) and the parametric models: ARX(autoregressive with exogenous inputs ), ARMAX (AutoRegressive Moving Average with eXogenous inputs ), OE(Output Error) and the Box-Jenkins (BJ)structure. The closed loop configuration was the R-V. The results showed that for the bottom

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Forrest, B. M. "Memory and optimisation in neural network models." Thesis, University of Edinburgh, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384164.

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A numerical study of two classes of neural network models is presented. The performance of Ising spin neural networks as content-addressable memories for the storage of bit patterns is analysed. By studying systems of increasing sizes, behaviour consistent with fintite-size scaling, characteristic of a first-order phase transition, is shown to be exhibited by the basins of attraction of the stored patterns in the Hopfield model. A local iterative learning algorithm is then developed for these models which is shown to achieve perfect storage of nominated patterns with near-optimal content-addre

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Hedar, Sara. "Applying Machine Learning Methods to Predict the Outcome of Shots in Football." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414774.

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The thesis investigates a publicly available dataset which covers morethan three million events in football matches. The aim of the study isto train machine learning models capable of modeling the relationshipbetween a shot event and its outcome. That is, to predict if a footballshot will result in a goal or not. By representing the shot indifferent ways, the aim is to draw conclusion regarding what elementsof a shot allows for a good prediction of its outcome. The shotrepresentation was varied both by including different numbers of eventspreceding the shot and by varying the set of features d

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Santos, Cinara de Jesus. "Avaliação do uso de classificadores para verificação de atendimento a critérios de seleção em programas sociais." Universidade Federal de Juiz de Fora (UFJF), 2017. https://repositorio.ufjf.br/jspui/handle/ufjf/5582.

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Submitted by isabela.moljf@hotmail.com (isabela.moljf@hotmail.com) on 2017-08-15T12:01:50Z No. of bitstreams: 1 cinaradejesussantos.pdf: 4566569 bytes, checksum: bddc2ea97276541c0a8ad30a371102d1 (MD5)<br>Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-08-15T12:02:54Z (GMT) No. of bitstreams: 1 cinaradejesussantos.pdf: 4566569 bytes, checksum: bddc2ea97276541c0a8ad30a371102d1 (MD5)<br>Made available in DSpace on 2017-08-15T12:02:54Z (GMT). No. of bitstreams: 1 cinaradejesussantos.pdf: 4566569 bytes, checksum: bddc2ea97276541c0a8ad30a371102d1 (MD5) Pr

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Alamgir, Nyma. "Computer vision based smoke and fire detection for outdoor environments." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/201654/1/Nyma_Alamgir_Thesis.pdf.

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Surveillance Video-based detection of outdoor smoke and fire has been a challenging task due to the chaotic variations of shapes, movement, colour, texture, and density. This thesis contributes to the advancement of the contemporary efforts of smoke and fire detection by proposing novel technical methods and their possible integration into a complete fire safety model. The novel contributions of this thesis include an efficient feature calculation method combining local and global texture properties, the development of deep learning-based models and a conceptual framework to incorporate weathe

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Nguyen, Minh Ha Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "Cooperative coevolutionary mixture of experts : a neuro ensemble approach for automatic decomposition of classification problems." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2006. http://handle.unsw.edu.au/1959.4/38752.

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Artificial neural networks have been widely used for machine learning and optimization. A neuro ensemble is a collection of neural networks that works cooperatively on a problem. In the literature, it has been shown that by combining several neural networks, the generalization of the overall system could be enhanced over the separate generalization ability of the individuals. Evolutionary computation can be used to search for a suitable architecture and weights for neural networks. When evolutionary computation is used to evolve a neuro ensemble, it is usually known as evolutionary neuro ense

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Brady, Patrick. "Internal representation and biological plausibility in an artificial neural network." Thesis, Brunel University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311273.

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Cunanan, Kevin. "Developing a Recurrent Neural Network with High Accuracy for Binary Sentiment Analysis." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/cmc_theses/1835.

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Sentiment analysis has taken on various machine learning approaches in order to optimize accuracy, precision, and recall. However, Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) account for the context of a sentence by using previous predictions as additional input for future sentence predictions. Our approach focused on developing an LSTM RNN that could perform binary sentiment analysis for positively and negatively labeled sentences. In collaboration with Mariam Salloum, I developed a collection of programs to classify individual sentences as either positive or negative. This

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Owen, Mark. "A binary recurrent self-organising neural network and its application to speech recognition." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259480.

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Narayanan, Arun. "Computational auditory scene analysis and robust automatic speech recognition." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1401460288.

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Nordén, Frans, and Reis Marlevi Filip von. "A Comparative Analysis of Machine Learning Algorithms in Binary Facial Expression Recognition." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254259.

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In this paper an analysis is conducted regarding whether a higher classification accuracy of facial expressions are possible. The approach used is that the seven basic emotional states are combined into a binary classification problem. Five different machine learning algorithms are implemented: Support vector machines, Extreme learning Machine and three different Convolutional Neural Networks (CNN). The utilized CNN:S were one conventional, one based on VGG16 and transfer learning and one based on residual theory known as RESNET50. The experiment was conducted on two datasets, one small contai

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El-Hage, Sebastian. "Predicting Purchase of Airline Seating Using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280461.

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With the continuing surge in digitalization within the travel industry and the increased demand of personalized services, understanding customer behaviour is becoming a requirement to survive for travel agencies. The number of cases that addresses this problem are increasing and machine learning is expected to be the enabling technique. This thesis will attempt to train two different models, a multi-layer perceptron and a support vector machine, to reliably predict whether a customer will add a seat reservation with their flight booking. The models are trained on a large dataset consisting of

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Faula, Yannick. "Extraction de caractéristiques sur des images acquises en contexte mobile : Application à la reconnaissance de défauts sur ouvrages d’art." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI077.

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Le réseau ferroviaire français dispose d’une infrastructure de grande ampleur qui se compose de nombreux ouvrages d’art. Ces derniers subissent les dégradations du temps et du trafic et font donc l’objet d’une surveillance périodique pour détecter l’apparition de défauts. Aujourd’hui, cette inspection se fait en grande partie, visuellement par des opérateurs experts. Plusieurs entreprises testent de nouveaux vecteurs d’acquisition photo comme le drone, destinés à la surveillance des ouvrages de génie civil. Dans cette thèse, l’objectif principal est de développer un système capable de détecter

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Beneš, Jiří. "Unární klasifikátor obrazových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442432.

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The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyper parameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of reimplementation of the unary classifier.

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Pavlík, Daniel. "Využití neuronové sítě při identifikaci znaku v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217295.

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This thesis is about using neural networks in recognition of letters A to Z and numbers 0 to 9. In the first part is theoretically described substance of neural networks and concretically described principle the method of learning multiple-layer network with backward spreaded error(a.ka Backpropagation). Basic problematic of processing the picture and resilence of network against degradation picture by a noise and compression JPEG is also described here. Second part is directed to practical realization of feed foward multiple-layer network with recognition the binary patterns of alphabetical l

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Martinek, Václav. "Rozpoznávání hudebních coververzí pomocí technik Music Information Retrieval." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442587.

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This master’s thesis deals with designs and implementation of systems for music cover recognition. The introduction part is devoted to the calculation parameters from audio signal using Music Information Retrieval techniques. Subsequently, various forms of cover versions and musical aspects that cover versions share are defined. The thesis also deals in detail with the creation and distribution of a database of cover versions. Furthermore, the work presents methods and techniques for comparing and processing the calculated parameters. Attention is then paid to the OTI method, CSM calculation a

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Ezzadeen, Mona. "Conception d'un circuit dédié au calcul dans la mémoire à base de technologie 3D innovante." Electronic Thesis or Diss., Aix-Marseille, 2022. http://theses.univ-amu.fr.lama.univ-amu.fr/221212_EZZADEEN_955e754k888gvxorp699jljcho_TH.pdf.

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Avec le développement de l'internet des objets et de l'intelligence artificielle, le "déluge de données" est une réalité, poussant au développement de systèmes de calcul efficaces énergétiquement. Dans ce contexte, en effectuant le calcul directement à l'intérieur ou à proximité des mémoires, le paradigme de l'in/near-memory-computing (I/NMC) semble être une voie prometteuse. En effet, les transferts de données entre les mémoires et les unités de calcul sont très énergivores. Cependant, les classiques mémoires Flash souffrent de problèmes de miniaturisation et ne semblent pas facilement adapté

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Gautam, Daniel. "Search for Stop using Machine Learning : A Bachelors Project in Physics." Thesis, Stockholms universitet, Fysikum, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-194876.

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In this thesis the application of machine learning algorithms as a tool in the search for top squark is studied. Two neural network models are trained with simulated stop events as signal against dileptonic and semi-leptonic top pair production events as background. There is a substantial class imbalance between the number of signal and background samples that are used. The performance of the neural network models are compared to the performance of a cut and count method. None of the models outperform the standard cut and count method.

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Deaney, Mogammat Waleed. "A Comparison of Machine Learning Techniques for Facial Expression Recognition." University of the Western Cape, 2018. http://hdl.handle.net/11394/6412.

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Magister Scientiae - MSc (Computer Science)<br>A machine translation system that can convert South African Sign Language (SASL) video to audio or text and vice versa would be bene cial to people who use SASL to communicate. Five fundamental parameters are associated with sign language gestures, these are: hand location; hand orientation; hand shape; hand movement and facial expressions. The aim of this research is to recognise facial expressions and to compare both feature descriptors and machine learning techniques. This research used the Design Science Research (DSR) methodology. A DS

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Murach, Thomas. "Monoscopic Analysis of H.E.S.S. Phase II Data on PSR B1259–63/LS 2883." Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18484.

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Cherenkov-Teleskope sind in der Lage, das schwache Cherenkovlicht aus Teilchenschauern zu detektieren, die von kosmischen Teilchen mit Energien von ca. 100 GeV bis 100 TeV in der Erdatmosphäre initiiert werden. Das Ziel ist die Detektion von Cherenkovlicht aus Schauern, die von Gammastrahlen erzeugt wurden, der größte Teil der Schauer stammt jedoch von geladenen Teilchen. Im Jahr 2012 wurde das H.E.S.S.-Observatorium in Namibia, bis dahin bestehend aus vier Teleskopen mit 100 m²-Spiegeln, um ein fünftes Teleskop mit einer Spiegelfläche von ca. 600 m² ergänzt. Aufgrund der großen Spiegelfläche

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Hutárek, Jiří. "Klasifikace objektů v obraze podle textury." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237277.

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Main subjects of this thesis are texture classification and texture-based object recognition. Various texture features are being explored, including several variants of local binary patterns (LBP). A novel modification of LBP (weighted spatial LBP) is proposed, with intention to improve on the spatial coverage of the traditional LBP. Rarely used color texture features are being discussed as well. Artificial neural networks and support vector machines are used to classify all the aforementioned features. Using these methods, framework for the texture classification and image segmentation is imp

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"Applications of neural networks in the binary classification problem." 1997. http://library.cuhk.edu.hk/record=b5889310.

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by Chan Pak Kei, Bernard.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.<br>Includes bibliographical references (leaves 125-127).<br>Chapter 1 --- Introduction --- p.10<br>Chapter 1.1 --- Overview --- p.10<br>Chapter 1.2 --- Classification Approaches --- p.11<br>Chapter 1.3 --- The Use of Neural Network --- p.12<br>Chapter 1.4 --- Motivations --- p.14<br>Chapter 1.5 --- Organization of Thesis --- p.16<br>Chapter 2 --- Related Work --- p.19<br>Chapter 2.1 --- Overview --- p.19<br>Chapter 2.2 --- Neural Network --- p.20<br>Chapter 2.2.1 --- Backpropagation Feedforward Neura

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TANG, CHI-HUAN, and 唐其煥. "Low-cost Design and Implementation for Binary Convolutional Neural Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/c5aa76.

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碩士<br>國立高雄應用科技大學<br>電子工程系<br>106<br>In recent years, deep learning has been one of the most popular subject in academia and widely used in many fields such as computer vision, image classification, motion recognition, voice recognition, and big-data analysis tasks. Although the larger neural network architecture can improve accuracy obviously, the cost of memory usage, power consumption and time consumption also increase. How to use memory and speed effectively to achieve a certain accuracy has been the most popular subject in recent years. In the first part of this thesis, we will introduce t

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"Approaches to the implementation of binary relation inference network." Chinese University of Hong Kong, 1994. http://library.cuhk.edu.hk/record=b5888221.

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by C.W. Tong.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.<br>Includes bibliographical references (leaves 96-98).<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- The Availability of Parallel Processing Machines --- p.2<br>Chapter 1.1.1 --- Neural Networks --- p.5<br>Chapter 1.2 --- Parallel Processing in the Continuous-Time Domain --- p.6<br>Chapter 1.3 --- Binary Relation Inference Network --- p.10<br>Chapter 2 --- Binary Relation Inference Network --- p.12<br>Chapter 2.1 --- Binary Relation Inference Network --- p.12<br>Chapter 2.1.1 --- Network Structure ---

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Srinivas, Suraj. "Learning Compact Architectures for Deep Neural Networks." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/3581.

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Deep neural networks with millions of parameters are at the heart of many state of the art computer vision models. However, recent works have shown that models with much smaller number of parameters can often perform just as well. A smaller model has the advantage of being faster to evaluate and easier to store - both of which are crucial for real-time and embedded applications. While prior work on compressing neural networks have looked at methods based on sparsity, quantization and factorization of neural network layers, we look at the alternate approach of pruning neurons. Training Neural N

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Srinivas, Suraj. "Learning Compact Architectures for Deep Neural Networks." Thesis, 2017. http://etd.iisc.ernet.in/2005/3581.

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Deep neural networks with millions of parameters are at the heart of many state of the art computer vision models. However, recent works have shown that models with much smaller number of parameters can often perform just as well. A smaller model has the advantage of being faster to evaluate and easier to store - both of which are crucial for real-time and embedded applications. While prior work on compressing neural networks have looked at methods based on sparsity, quantization and factorization of neural network layers, we look at the alternate approach of pruning neurons. Training Neural N

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Lian, Chi-Li, and 連崔立. "Using Probabilistic Neural Networks and Binary Sequence Algorithm to Build Financial Prediction Models - A Case of the Electronic Industry in Taiwan." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/3t54vc.

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碩士<br>國立臺北科技大學<br>工業工程與管理研究所<br>96<br>This research attempts to use probabilistic neural networks(PNN) and binary sequence algorithm(BSA) to build financial prediction models, regard listed company as the research object, take three annual financial materials of company. The main purpose to build this financial prediction models, lie in finding the potential financial crisis inside enterprises ahead of time, offer investors and electronic industry one to consult alert news by this. This research is divided into two stages and built the model, the first stage is to use two kinds of data type an

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Lin, Zhouhan. "Deep neural networks for natural language processing and its acceleration." Thèse, 2019. http://hdl.handle.net/1866/23438.

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Cette thèse par article comprend quatre articles qui contribuent au domaine de l'apprentissage profond, en particulier à l'accélération de l’apprentissage par le biais de réseaux à faible précision et à l'application de réseaux de neurones profonds au traitement du langage naturel. Dans le premier article, nous étudions un schéma d’entraînement de réseau de neurones qui élimine la plupart des multiplications en virgule flottante. Cette approche consiste à binariser ou à ternariser les poids dans la propagation en avant et à quantifier les états cachés dans la propagation arrière, ce qui con

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Gurioli, Gianmarco. "Adaptive Regularisation Methods under Inexact Evaluations for Nonconvex Optimisation and Machine Learning Applications." Doctoral thesis, 2021. http://hdl.handle.net/2158/1238314.

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The major aim of this research thesis is to handle two main challenges arising when solving unconstrained optimisation problems with second-order methods: the reduction of the per-iteration cost and the stochastic analysis of the resulting non- deterministic algorithms. This is motivated by the fact that second-order procedures can be more efficient than first-order ones on badly scaled and ill-conditioned problems, since they seem to potentially take advantage of curvature information to easier escape from saddle points, being more robust to the choice of hyperparameters and the parameters tu

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Croon, Dennis Gerardus. "The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks." Master's thesis, 2020. http://hdl.handle.net/10362/103901.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>Extensive recent research has shown the importance of innovation in medical healthcare, with a focus on Pneumonia. It is vital and lifesaving to predict Pneumonia cases as fast as possible and preferably in advance of the symptoms. An online database source managed to gather Pneumonia-specific image data, with not just the presence of the infection, but also the nature of it, divided in bacterial- and viral infection. The first achievement is extracting valuable inf

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Lin, Jia-Wei, and 林佳緯. "High Accuracy RRAM-Based Binary Neural Network Training." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/g5t559.

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碩士<br>國立交通大學<br>電子研究所<br>107<br>Deep learning is getting popular at the edge devices to provide better services. However, its high computational complexity and memory bandwidth makes real time execution on the traditional Von Neuman architecture very challenging even with the low complexity binary neural network model. A promising solution, in-memory computing, computes directly in memory with high parallelism and reduces the data transmission time, especially with the emerging new device such as Resistive Random Access Memory (RRAM) due to its small area and low power nature. However, RRAM de

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Hui-LiangYu and 游輝亮. "Dual Path Binary Neural Network with Adaptive Output Range." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/nuydfp.

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碩士<br>國立成功大學<br>資訊工程學系<br>106<br>In recent years, deep neural networks (DNNs) have achieved state-of-the-art results in the fields of image recognition, semantic segmentation and machine translation. However, powerful DNNs usually have a large number of parameters and complex calculations. For instance, ImageNet classification challenge winner in 2012, Alex Net, has a model size of about 249MB and 60 million parameters, which needs to perform about 1.5 billion FLOPs to classify a 224 x 224 image. While perform such complex computations, GPUs based machines usually used to speed up training pro

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