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dc.contributorKleinsteuber, Martin
dc.contributor.authorSafari, Pooyan
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.descriptionProjecte realitzat en el marc d’un programa de mobilitat amb la Technische Universität München (TUM)
dc.description.abstractIn recent years, deep learning has opened a new research line in pattern recognition tasks. It has been hypothesized that this kind of learning would capture more abstract patterns concealed in data. It is motivated by the new findings both in biological aspects of the brain and hardware developments which have made the parallel processing possible. Deep learning methods come along with the conventional algorithms for optimization and training make them efficient for variety of applications in signal processing and pattern recognition. This thesis explores these novel techniques and their related algorithms. It addresses and compares different attributes of these methods, sketches in their possible advantages and disadvantages.
dc.publisherUniversitat Politècnica de Catalunya
dc.publisherTechnische Universität München
dc.rightsS'autoritza la difusió de l'obra mitjançant la llicència Creative Commons o similar 'Reconeixement-NoComercial- SenseObraDerivada'
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes
dc.subject.lcshDigital communications
dc.subject.lcshControl theory
dc.subject.lcshComplex systems and interdisciplinary science
dc.subject.lcshPattern recognition systems
dc.titleDeep Learning For Sequential Pattern Recognition
dc.typeMaster thesis
dc.subject.lemacComunicacions digitals
dc.subject.lemacControl, Teoria de
dc.subject.lemacSistemes complexos
dc.subject.lemacReconeixement de formes (Informàtica)
dc.rights.accessOpen Access
dc.audience.mediatorEscola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona
dc.contributor.covenanteeTechnische Universität München

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