Tesis sobre el tema "Pattern recognition"
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An, Kyung Hee. "Concurrent Pattern Recognition and Optical Character Recognition". Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332598/.
Texto completoYao, Xiaoqiang. "Pattern-recognition scheduling". Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1177698616.
Texto completoPetheram, R. J. "Automatic pattern recognition". Thesis, University of Nottingham, 1989. http://eprints.nottingham.ac.uk/28974/.
Texto completoChoakjarernwanit, Naruetep. "Statistical pattern recognition". Thesis, University of Surrey, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306586.
Texto completoPlacide, Eustache. "Hybrid pattern recognition". DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 1987. http://digitalcommons.auctr.edu/dissertations/3018.
Texto completoSmeraldi, Fabrizio. "Attention-driven pattern recognition /". [S.l.] : [s.n.], 2000. http://library.epfl.ch/theses/?nr=2153.
Texto completoAngstenberger, Larisa. "Dynamic fuzzy pattern recognition". [S.l.] : [s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=962701106.
Texto completoWeir, D. K. "Pattern recognition of electrocardiograms". Thesis, University of Ulster, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355922.
Texto completoLindén, Fredrik. "Fractal pattern recognition and recreation". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-181224.
Texto completoSun, Te-Wei. "DEPARS, design pattern recognition system". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq28464.pdf.
Texto completoHoang, Thai V. "Image Representations for Pattern Recognition". Phd thesis, Université Nancy II, 2011. http://tel.archives-ouvertes.fr/tel-00714651.
Texto completoWong, K. H. "Dynamic programming in pattern recognition". Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383059.
Texto completoKinna, David John. "Pattern recognition in chemical crystallography". Thesis, University of Oxford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318724.
Texto completoDaemi, M. F. "Information theory and pattern recognition". Thesis, University of Nottingham, 1990. http://eprints.nottingham.ac.uk/14003/.
Texto completoSardana, H. K. "Edge moments in pattern recognition". Thesis, University of Nottingham, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357101.
Texto completoFüllen, Georg Karl-Heinz. "Protein engineering and pattern recognition". Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/17354.
Texto completoSTOSIC, Dusan. "q-Gaussians for pattern recognition". Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/17361.
Texto completoMade available in DSpace on 2016-07-13T19:23:52Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dusan Stosic - dissertacao de mestrado.pdf: 6434406 bytes, checksum: db312999879f1c3ebb1795ce764a272e (MD5) Previous issue date: 2016-03-01
CAPES
Pattern recognition plays an important role for solving many problems in our everyday lives: from simple tasks such as reading texts to more complex ones like driving cars. Subconsciously, the recognition of patterns is instantaneous and an innate ability to every human. However, programming (or “teaching”) a machine how to do the same can present an incredibly difficult task. There are many situations where irrelevant or misleading patterns, poorly represented classes, and complex decision boundaries make recognition very hard, or even impossible by current standards. Important contributions to the field of pattern recognition have been attained through the adoption of methods of statistical mechanics, which has paved the road for much of the research done in academia and industry, ranging from the revival of connectionism to modern day deep learning. Yet traditional statistical mechanics is not universal and has a limited domain of applicability - outside this domain it can make wrong predictions. Non-extensive statistical mechanics has recently emerged to cover a variety of anomalous situations that cannot be described within standard Boltzmann-Gibbs theory, such as non-ergodic systems characterized by long-range interactions, or long-term memories. The literature on pattern recognition is vast, and scattered with applications of non-extensive statistical mechanics. However, most of this work has been done using non-extensive entropy, and little can be found on practical applications of other non-extensive constructs. In particular, non-extensive entropy is widely used to improve segmentation of images that possess strongly correlated patterns, while only a small number of works employ concepts other than entropy for solving similar recognition tasks. The main goal of this dissertation is to expand applications of non-extensive distributions, namely the q-Gaussian, in pattern recognition. We present ourcontributions in the form of two (published) articles where practical uses of q-Gaussians are explored in neural networks. The first paper introduces q Gaussian transfer functions to improve classification of random neural networks, and the second paper extends this work to ensembles which involves combining a set of such classifiers via majority voting.
Reconhecimento de padrões tem um papel importante na solução de diversos problemas no nosso quotidiano: a partir de tarefas simples como ler textos, até as mais complexas como dirigir carros. Inconscientemente, o reconhecimento de padrões pelo cérebro é instantâneo, representando uma habilidade inata de cada ser humano. No entanto, programar (ou “ensinar”) uma máquina para fazer o mesmo pode se tornar uma tarefa extremamente difícil. Há muitas situações onde padrões irrelevantes ou enganosos, classes mal representadas, ou bordas de decisões complexas, tornam o reconhecimento muito difícil, ou mesmo impossível pelos padrões atuais. Diversas contribuições importantes na área de reconhecimento de padrões foram alcançadas através da aplicação de métodos provenientes da mecânica estatística, que estimularam uma grande parte da pesquisa conduzida na academia bem como na indústria, desde o renascimento de conexionismo até o moderno conceito de “deep learning”. No entanto, a mecânica estatística tradicional não é universal e tem um domínio de aplicação limitado - fora deste domínio ela pode fazer previsões erradas. A mecânica estatística não-extensiva surgiu recentemente para atender uma variedade de situações anômalas que não podem ser descritas de forma adequada com a teoria de Boltzmann-Gibbs, tais como sistemas não-ergódicos, caracterizadas por interações de longo alcance, ou memórias de longo prazo. A literatura sobre reconhecimento de padrões é vasta, e dispersa com aplicações da mecânica estatística não-extensiva. No entanto, a maioria destes trabalhos utilizam a entropia não-extensiva, e existem poucas aplicações práticas de outros conceitos não-extensivos. Em particular, a entropia não extensiva é amplamente usada para aperfeiçoar segmentação de imagens que possuem padrões fortemente correlacionados, enquanto apenas um pequeno número de trabalhos empregam outros conceitos não-extensivos para resolver tarefas semelhantes. O objetivo principal desta dissertação é expandir aplicações de distribuições não-extensivas, como a q-Gaussiana, em reconhecimento de padrões. Nos apresentamos as nossas contribuições no formato de dois artigos (publicados) onde exploramos usos práticos da q-Gaussiana em redes neurais. O primeiro artigo introduz funções de transferência baseados na q-Gaussiana para aperfeiçoar a classificação de redes neurais aleatórias, e o segundo artigo estende este trabalho para ensembles, onde um conjunto de tais classificadores são combinados através de votação por maioria.
Ihnatenko, N. V. "Systems for automatic pattern recognition". Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/34837.
Texto completoDover, Kathryn. "Pattern Recognition in Stock Data". Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/hmc_theses/105.
Texto completoMartino, Federico De. "Pattern recognition of brain signals". [Maastricht] : [Maastricht University], 2008. http://arno.unimaas.nl/show.cgi?fid=13359.
Texto completoBurles, Nathan. "Pattern recognition using associative memories". Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/7368/.
Texto completoCalvo-Zaragoza, Jorge. "Pattern Recognition for Music Notation". Doctoral thesis, Universidad de Alicante, 2016. http://hdl.handle.net/10045/63415.
Texto completoUL-ISLAM, IHTESHAM. "Feature Fusion for Pattern Recognition". Doctoral thesis, Politecnico di Torino, 2015. http://hdl.handle.net/11583/2592755.
Texto completoOu, Chung-Pei. "Protein array for small molecules recognition using pattern recognition". Thesis, Imperial College London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.420941.
Texto completoAl, Rifaee Mustafa Moh'd Husien. "Unconstrained iris recognition". Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10949.
Texto completoRobinson, Daniel D. "Applications of pattern recognition and pattern analysis to molecule design". Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343465.
Texto completoMankoff, Jennifer C. "An architecture and interaction techniques for handling ambiguity in recognition-based input". Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/8214.
Texto completoMahmood, A. "Automatic drawing recognition". Thesis, University of Nottingham, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.381072.
Texto completoScott, Emily A. "Recognition of aerospace acoustic sources using advanced pattern recognition techniques". Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-03022010-020131/.
Texto completoSuh, Bongwon. "Image management using pattern recognition systems". College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2455.
Texto completoThesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Hall, Arthur Daniel. "Pipelined image processing for pattern recognition". Thesis, University of Cambridge, 1992. https://www.repository.cam.ac.uk/handle/1810/251523.
Texto completoColven, David Michael. "Tactile pattern recognition using neural networks". Thesis, University of Ottawa (Canada), 1993. http://hdl.handle.net/10393/6513.
Texto completoZieba, Maciej. "Multistage neural networks for pattern recognition". Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2087.
Texto completoKleyko, Denis. "Pattern Recognition with Vector Symbolic Architectures". Licentiate thesis, Luleå tekniska universitet, Datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17439.
Texto completoGodkänd; 2016; 20160207 (denkle); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Denis Kleyko Ämne: Kommunikations- och beräkningssystem / Dependable Communication and Computation Systems Uppsats: Pattern Recognition with Vector Symbolic Architectures Examinator: Professor Evgeny Osipov Institutionen för system- och rymdteknik, Avdelning: Datavetenskap, Luleå tekniska universitet. Diskutant: Associate Professor Okko Räsänen, Aalto University, Department of Signal Processing and Acoustics, Finland. Tid: Måndag 21 mars, 2016 kl 10.00 Plats: A109, Luleå tekniska universitet
Wang, Qun. "Bootstrap techniques for statistical pattern recognition". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0027/MQ52407.pdf.
Texto completoSherrah, Jamie. "Automatic feature extraction for pattern recognition /". Title page, contents and abstract only, 1998. http://web4.library.adelaide.edu.au/theses/09PH/09phs553.pdf.
Texto completoCD-ROM in back pocket comprises experimental results and executables. Includes bibliographical references (p. 251-261).
Surkov, David. "Inductive confidence machine for pattern recognition". Thesis, Royal Holloway, University of London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412337.
Texto completoElliffe, Martin C. M. "Neural networks for Invariant pattern recognition". Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302530.
Texto completoRagothaman, Pradeep. "EFFICIENT ALGORITHMS FOR CORRELATION PATTERN RECOGNITION". Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2132.
Texto completoPh.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
Thompson, J. R. "Applications of pattern recognition in medicine". Thesis, Open University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377939.
Texto completoChoakjarernwanit, Naruetep. "Feature selection in statistical pattern recognition". Thesis, University of Surrey, 1992. http://epubs.surrey.ac.uk/843569/.
Texto completoZhendong, Wang. "Error Pattern Recognition Using Machine Learning". Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150589.
Texto completoLucas, Simon Mark. "Connectionist architectures for syntactic pattern recognition". Thesis, University of Southampton, 1991. https://eprints.soton.ac.uk/256263/.
Texto completoKou, Yufeng. "Abnormal Pattern Recognition in Spatial Data". Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30145.
Texto completoPh. D.
Tembe, Waibhav D. "Proximity Metrics for Contextual Pattern Recognition". University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1096665126.
Texto completoVillegas, Santamaría Mauricio. "Contributions to High-Dimensional Pattern Recognition". Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/10939.
Texto completoVillegas Santamaría, M. (2011). Contributions to High-Dimensional Pattern Recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10939
Palancia
Siddique, Nahian A. "PATTERN RECOGNITION IN CLASS IMBALANCED DATASETS". VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4480.
Texto completoSuliman, Ayman y Joakim Bäverlind. "Experiments With Four Pattern Recognition Algorithms". Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214730.
Texto completoEl, Ghawalby Heyayda. "Spectral geometry for structural pattern recognition". Thesis, University of York, 2011. http://etheses.whiterose.ac.uk/1525/.
Texto completoDannenberg, Matthew. "Pattern Recognition in High-Dimensional Data". Scholarship @ Claremont, 2016. https://scholarship.claremont.edu/hmc_theses/76.
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