Bibliografías temáticas / Neural networks / Tesis
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Tesis sobre el tema "Neural networks"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 25 de julio de 2025

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1

Patterson, Raymond A. "Hybrid Neural networks and network design." Connect to resource, 1995. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262707683.

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2

Rastogi, Preeti. "Assessing Wireless Network Dependability Using Neural Networks." Ohio University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1129134364.

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3

Chambers, Mark Andrew. "Queuing network construction using artificial neural networks /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488193665234291.

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4

Dunn, Nathan A. "A Novel Neural Network Analysis Method Applied to Biological Neural Networks." Thesis, view abstract or download file of text, 2006. http://proquest.umi.com/pqdweb?did=1251892251&sid=2&Fmt=2&clientId=11238&RQT=309&VName=PQD.

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Thesis (Ph. D.)--University of Oregon, 2006.<br>Typescript. Includes vita and abstract. Includes bibliographical references (leaves 122- 131). Also available for download via the World Wide Web; free to University of Oregon users.
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5

Dong, Yue. "Higher Order Neural Networks and Neural Networks for Stream Learning." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35731.

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The goal of this thesis is to explore some variations of neural networks. The thesis is mainly split into two parts: a variation of the shaping functions in neural networks and a variation of learning rules in neural networks. In the first part, we mainly investigate polynomial perceptrons - a perceptron with a polynomial shaping function instead of a linear one. We prove the polynomial perceptron convergence theorem and illustrate the notion by showing that a higher order perceptron can learn the XOR function through empirical experiments with implementation. In the second part, we propose
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6

Xu, Shuxiang, University of Western Sydney, and of Informatics Science and Technology Faculty. "Neuron-adaptive neural network models and applications." THESIS_FIST_XXX_Xu_S.xml, 1999. http://handle.uws.edu.au:8081/1959.7/275.

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Artificial Neural Networks have been widely probed by worldwide researchers to cope with the problems such as function approximation and data simulation. This thesis deals with Feed-forward Neural Networks (FNN's) with a new neuron activation function called Neuron-adaptive Activation Function (NAF), and Feed-forward Higher Order Neural Networks (HONN's) with this new neuron activation function. We have designed a new neural network model, the Neuron-Adaptive Neural Network (NANN), and mathematically proved that one NANN can approximate any piecewise continuous function to any desired accuracy
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7

Allen, T. J. "Optoelectronic neural networks." Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362900.

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8

Sloan, Cooper Stokes. "Neural bus networks." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119711.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 65-68).<br>Bus schedules are unreliable, leaving passengers waiting and increasing commute times. This problem can be solved by modeling the traffic network, and delivering predicted arrival times to passengers. Research att
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9

Boychenko, I. V., and G. I. Litvinenko. "Artificial neural networks." Thesis, Вид-во СумДУ, 2009. http://essuir.sumdu.edu.ua/handle/123456789/17044.

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10

Landry, Kenneth D. "Evolutionary neural networks." Thesis, Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/51904.

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To create neural networks that work, one needs to specify a structure and the interconnection weights between each pair of connected computing elements. The structure of a network can be selected by the designer depending on the application, although the selection of interconnection weights is a much larger problem. Algorithms have been developed to alter the weights slightly in order to produce the desired results. Learning algorithms such as Hebb's rule, the Delta rule and error propagation have been used, with success, to learn the appropriate weights. The major objection to this class of a
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11

Viñoles, Serra Mireia. "Dynamics of Two Neuron Cellular Neural Networks." Doctoral thesis, Universitat Ramon Llull, 2011. http://hdl.handle.net/10803/9154.

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Les xarxes neuronals cel·lulars altrament anomenades CNNs, són un tipus de sistema dinàmic que relaciona diferents elements que s'anomenen neurones via unes plantilles de paràmetres. Aquest sistema queda completament determinat coneixent quines són les entrades a la xarxa, les sortides i els paràmetres o pesos. En aquest treball fem un estudi exhaustiu sobre aquest tipus de xarxa en el cas més senzill on només hi intervenen dues neurones. Tot i la simplicitat del sistema, veurem que pot tenir una dinàmica molt rica. <br/><br/>Primer de tot, revisem l'estabilitat d'aquest sistema des de dos pun
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12

Ellerbrock, Thomas M. "Multilayer neural networks learnability, network generation, and network simplification /." [S.l. : s.n.], 1999. http://deposit.ddb.de/cgi-bin/dokserv?idn=958467897.

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13

Ayoub, Issa. "Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39337.

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Affective computing has gained significant attention from researchers in the last decade due to the wide variety of applications that can benefit from this technology. Often, researchers describe affect using emotional dimensions such as arousal and valence. Valence refers to the spectrum of negative to positive emotions while arousal determines the level of excitement. Describing emotions through continuous dimensions (e.g. valence and arousal) allows us to encode subtle and complex affects as opposed to discrete emotions, such as the basic six emotions: happy, anger, fear, disgust, sad and n
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14

Chen, Prakoon. "The Neural Shell : a neural networks simulator." Connect to resource, 1989. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1228839518.

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15

Bolt, George Ravuama. "Fault tolerance in artificial neural networks : are neural networks inherently fault tolerant?" Thesis, University of York, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317683.

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16

Reis, Elohim Fonseca dos 1984. "Criticality in neural networks = Criticalidade em redes neurais." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276917.

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Orientadores: José Antônio Brum, Marcus Aloizio Martinez de Aguiar<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin<br>Made available in DSpace on 2018-08-29T15:40:55Z (GMT). No. of bitstreams: 1 Reis_ElohimFonsecados_M.pdf: 2277988 bytes, checksum: 08f2c3b84a391217d575c0f425159fca (MD5) Previous issue date: 2015<br>Resumo: Este trabalho é dividido em duas partes. Na primeira parte, uma rede de correlação é construída baseada em um modelo de Ising em diferentes temperaturas, crítica, subcrítica e supercrítica, usando um algorítimo de Metropolis
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17

Flanagan, John Adrian. "Self-organising neural networks /." [S.l.] : [s.n.], 1994. http://library.epfl.ch/theses/?nr=1306.

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18

Kocheisen, Michael. "Neural networks in photofinishing /." Zürich, 1997. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=11985.

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19

Wendemuth, Andreas. "Optimisation in neural networks." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386749.

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20

Corbett-Clark, Timothy Alexander. "Explanation from neural networks." Thesis, University of Oxford, 1998. http://ora.ox.ac.uk/objects/uuid:b94d702a-1243-4702-b751-68784c855ab2.

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Neural networks have frequently been found to give accurate solutions to hard classification problems. However neural networks do not make explained classifications because the class boundaries are implicitly defined by the network weights, and these weights do not lend themselves to simple analysis. Explanation is desirable because it gives problem insight both to the designer and to the user of the classifier. Many methods have been suggested for explaining the classification given by a neural network, but they all suffer from one or more of the following disadvantages: a lack of equivalence
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21

Glackin, Cornelius. "Fuzzy spiking neural networks." Thesis, University of Ulster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.505831.

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22

Pritchett, William Christopher. "Neural networks for classification." Thesis, Monterey, California. Naval Postgraduate School, 1998. http://hdl.handle.net/10945/8735.

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CIVINS<br>In many applications, ranging from character recognition to signal detection to automatic target identification, the problem of signal classification is of interest. Often, for example, a signal is known to belong to one of a family of sets C sub 1..., C sub n and the goal is to classify the signal according to the set to which it belongs. The main purpose of this thesis is to show that under certain conditions placed on the sets, the theory of uniform approximation can be applied to solve this problem. Specifically, if we assume that sets C sub j are compact subsets of a normed line
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23

Menneer, Tamaryn Stable Ia. "Quantum artificial neural networks." Thesis, University of Exeter, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286530.

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24

Tattersall, Graham David. "Neural networks and generalisation." Thesis, University of East Anglia, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266735.

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25

Liu, Qian. "Deep spiking neural networks." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/deep-spiking-neural-networks(336e6a37-2a0b-41ff-9ffb-cca897220d6c).html.

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Neuromorphic Engineering (NE) has led to the development of biologically-inspired computer architectures whose long-term goal is to approach the performance of the human brain in terms of energy efficiency and cognitive capabilities. Although there are a number of neuromorphic platforms available for large-scale Spiking Neural Network (SNN) simulations, the problem of programming these brain-like machines to be competent in cognitive applications still remains unsolved. On the other hand, Deep Learning has emerged in Artificial Neural Network (ANN) research to dominate state-of-the-art solutio
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26

Kalchbrenner, Nal. "Encoder-decoder neural networks." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:d56e48db-008b-4814-bd82-a5d612000de9.

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This thesis introduces the concept of an encoder-decoder neural network and develops architectures for the construction of such networks. Encoder-decoder neural networks are probabilistic conditional generative models of high-dimensional structured items such as natural language utterances and natural images. Encoder-decoder neural networks estimate a probability distribution over structured items belonging to a target set conditioned on structured items belonging to a source set. The distribution over structured items is factorized into a product of tractable conditional distributions over in
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27

Nyamapfene, Abel. "Unsupervised multimodal neural networks." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/844064/.

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We extend the in-situ Hebbian-linked SOMs network by Miikkulainen to come up with two unsupervised neural networks that learn the mapping between the individual modes of a multimodal dataset. The first network, the single-pass Hebbian linked SOMs network, extends the in-situ Hebbian-linked SOMs network by enabling the Hebbian link weights to be computed through one- shot learning. The second network, a modified counter propagation network, extends the unsupervised learning of crossmodal mappings by making it possible for only one self-organising map to implement the crossmodal mapping. The two
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28

Remmelzwaal, Leendert Amani. "Salience-affected neural networks." Master's thesis, University of Cape Town, 2009. http://hdl.handle.net/11427/12111.

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Includes abstract.<br>Includes bibliographical references (leaves 46-49).<br>In this research, the salience of an entity refers to its state or quality of standing out, or receiving increased attention, relative to neighboring entities. By neighbouring entities we refer to both spatial (i.e. similar visual objects) and temporal (i.e. related concepts). In this research we model the effect of non-local connections using an ANN, creating a salience-affected neural network (SANN). We adapt an ANN to embody the capacity to respond to an input salience signal and to produce a reverse salience signa
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29

Cheung, Ka Kit. "Neural networks for optimization." HKBU Institutional Repository, 2001. http://repository.hkbu.edu.hk/etd_ra/291.

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30

Suárez-Varela, Macià José Rafael. "Enabling knowledge-defined networks : deep reinforcement learning, graph neural networks and network analytics." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/669212.

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Significant breakthroughs in the last decade in the Machine Learning (ML) field have ushered in a new era of Artificial Intelligence (AI). Particularly, recent advances in Deep Learning (DL) have enabled to develop a new breed of modeling and optimization tools with a plethora of applications in different fields like natural language processing, or computer vision. In this context, the Knowledge-Defined Networking (KDN) paradigm highlights the lack of adoption of AI techniques in computer networks and – as a result – proposes a novel architecture that relies on Software-Defined Networking (
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31

Turk, Fethi. "Improvements To Neural Network Based Restoration In Optical Networks." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609491/index.pdf.

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Performance of neural network based restoration of optical networks is evaluated and a few possible improvements are proposed. Neural network based restoration is simulated with optical link capacities assigned by a new method. Two new improvement methods are developed to reduce the neural network size and the restoration time of severed optical connections. Cycle based restoration is suggested, which reduces the neural network structure by restoring the severed connections for each optical node, iteratively. Additionally, to reduce the restoration time, the necessary waiting time before the n
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32

Post, David L. "Network Management: Assessing Internet Network-Element Fault Status Using Neural Networks." Ohio : Ohio University, 2008. http://www.ohiolink.edu/etd/view.cgi?ohiou1220632155.

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33

Brande, Julia K. Jr. "Computer Network Routing with a Fuzzy Neural Network." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29685.

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The growing usage of computer networks is requiring improvements in network technologies and management techniques so users will receive high quality service. As more individuals transmit data through a computer network, the quality of service received by the users begins to degrade. A major aspect of computer networks that is vital to quality of service is data routing. A more effective method for routing data through a computer network can assist with the new problems being encountered with today's growing networks. Effective routing algorithms use various techniques to determine the most
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34

Donachy, Shaun. "Spiking Neural Networks: Neuron Models, Plasticity, and Graph Applications." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3984.

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Networks of spiking neurons can be used not only for brain modeling but also to solve graph problems. With the use of a computationally efficient Izhikevich neuron model combined with plasticity rules, the networks possess self-organizing characteristics. Two different time-based synaptic plasticity rules are used to adjust weights among nodes in a graph resulting in solutions to graph prob- lems such as finding the shortest path and clustering.
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35

Varoonchotikul, Pichaid. "Flood forecasting using artificial neural networks /." Lisse : Balkema, 2003. http://www.e-streams.com/es0704/es0704_3168.html.

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36

SUSI, GIANLUCA. "Asynchronous spiking neural networks: paradigma generale e applicazioni." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2012. http://hdl.handle.net/2108/80567.

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37

Zaghloul, Waleed A. Lee Sang M. "Text mining using neural networks." Lincoln, Neb. : University of Nebraska-Lincoln, 2005. http://0-www.unl.edu.library.unl.edu/libr/Dissertations/2005/Zaghloul.pdf.

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Thesis (Ph.D.)--University of Nebraska-Lincoln, 2005.<br>Title from title screen (sites viewed on Oct. 18, 2005). PDF text: 100 p. : col. ill. Includes bibliographical references (p. 95-100 of dissertation).
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38

Buratti, Luca. "Visualisation of Convolutional Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Le Reti Neurali, e in particolare le Reti Neurali Convoluzionali, hanno recentemente dimostrato risultati straordinari in vari campi. Purtroppo, comunque, non vi è ancora una chiara comprensione del perchè queste architetture funzionino così bene e soprattutto è difficile spiegare il comportamento nel caso di fallimenti. Questa mancanza di chiarezza è quello che separa questi modelli dall’essere applicati in scenari concreti e critici della vita reale, come la sanità o le auto a guida autonoma. Per questa ragione, durante gli ultimi anni sono stati portati avanti diversi studi in modo tale d
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39

Rizzi, Giacomo. "Genetic Evolution of Neural Networks." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16769/.

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Evolutionary computation has been around ever since the late 50s. This thesis aims at elaborate on genetic algorithms, a subset of evolutionary computation, with particular regard to the field of neuroevolution, which is the application of GAs to the generation of functioning neural networks. The most widely adopted techniques are thereby explained and contrasted. The experimentation chapter finally shows an implementation of a genetic algorithm, inspired by existing algorithms, with the objective of optimizing a novel kind of artificial neural network.
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40

Squadrani, Lorenzo. "Deep neural networks and thermodynamics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Deep learning is the most effective and used approach to artificial intelligence, and yet it is far from being properly understood. The understanding of it is the way to go to further improve its effectiveness and in the best case to gain some understanding of the "natural" intelligence. We attempt a step in this direction with the aim of physics. We describe a convolutional neural network for image classification (trained on CIFAR-10) within the descriptive framework of Thermodynamics. In particular we define and study the temperature of each component of the network. Our results provides a n
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41

Thom, Markus [Verfasser]. "Sparse neural networks / Markus Thom." Ulm : Universität Ulm. Fakultät für Ingenieurwissenschaften und Informatik, 2015. http://d-nb.info/1067496319/34.

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42

Mancevo, del Castillo Ayala Diego. "Compressing Deep Convolutional Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217316.

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Deep Convolutional Neural Networks and "deep learning" in general stand at the cutting edge on a range of applications, from image based recognition and classification to natural language processing, speech and speaker recognition and reinforcement learning. Very deep models however are often large, complex and computationally expensive to train and evaluate. Deep learning models are thus seldom deployed natively in environments where computational resources are scarce or expensive. To address this problem we turn our attention towards a range of techniques that we collectively refer to as "mo
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43

Wenström, Sean, and Erik Ihrén. "Stock Trading with Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168095.

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Stock trading is increasingly done pseudo-automatically orfully automatically, using algorithms which make day-todayor even moment-to-moment decisions.This report investigates the possibility of creating a virtualstock trader, using a method used in Artificial Intelligence,called Neural Networks, to make intelligent decisions onwhen to buy and sell stocks on the stock market.We found that it might be possible to earn money overa longer period of time, although the profit is less than theaverage stock index. However, the method also performedwell in situations where the stock index is going dow
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44

Fridborn, Fredrik. "Reading Barcodes with Neural Networks." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143477.

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Barcodes are ubiquituous in modern society and they have had industrial application for decades. However, for noisy images modern methods can underperform. Poor lighting conditions, occlusions and low resolution can be problematic in decoding. This thesis aims to solve this problem by using neural networks, which have enjoyed great success in many computer vision competitions the last years. We investigate how three different networks perform on data sets with noisy images. The first network is a single classifier, the second network is an ensemble classifier and the third is based on a pre-tr
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45

Polhill, John Gareth. "Guaranteeing generalisation in neural networks." Thesis, University of St Andrews, 1995. http://hdl.handle.net/10023/12878.

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Neural networks need to be able to guarantee their intrinsic generalisation abilities if they are to be used reliably. Mitchell's concept and version spaces technique is able to guarantee generalisation in the symbolic concept-learning environment in which it is implemented. Generalisation, according to Mitchell, is guaranteed when there is no alternative concept that is consistent with all the examples presented so far, except the current concept, given the bias of the user. A form of bidirectional convergence is used by Mitchell to recognise when the no-alternative situation has been reached
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46

Rahman, Sardar Muhammad Monzurur, and mrahman99@yahoo com. "Data Mining Using Neural Networks." RMIT University. Electrical & Computer Engineering, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080813.094814.

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Data mining is about the search for relationships and global patterns in large databases that are increasing in size. Data mining is beneficial for anyone who has a huge amount of data, for example, customer and business data, transaction, marketing, financial, manufacturing and web data etc. The results of data mining are also referred to as knowledge in the form of rules, regularities and constraints. Rule mining is one of the popular data mining methods since rules provide concise statements of potentially important information that is easily understood by end users and also actionable pat
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47

Salama, Rameri. "On evolving modular neural networks." University of Western Australia. Dept. of Computer Science, 2000. http://theses.library.uwa.edu.au/adt-WU2003.0011.

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The basis of this thesis is the presumption that while neural networks are useful structures that can be used to model complex, highly non-linear systems, current methods of training the neural networks are inadequate in some problem domains. Genetic algorithms have been used to optimise both the weights and architectures of neural networks, but these approaches do not treat the neural network in a sensible manner. In this thesis, I define the basis of computation within a neural network as a single neuron and its associated input connections. Sets of these neurons, stored in a matrix
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48

Bhattacharya, Dipankar. "Neural networks for signal processing." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq21924.pdf.

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49

Krishnapura, Venugopal G. "Neural networks in process control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq28502.pdf.

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50

Wang, Fengzhen. "Neural networks for data fusion." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ30179.pdf.

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