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dc.contributorSturzel, Marc
dc.contributorRoux, Michel
dc.contributor.authorGarcia Cifuentes, Cristina
dc.date.accessioned2014-03-11T07:47:39Z
dc.date.available2014-03-11T07:47:39Z
dc.date.issued2009-06-10
dc.identifier.urihttp://hdl.handle.net/2099.1/20888
dc.descriptionTreball realitzat a TELECOM ParisTech i EADS France
dc.description.abstractMulti-class classification is the core issue of many pattern recognition tasks. Several applications require high-end machine learning solutions to provide satisfying results in operational contexts. However, most efficient ones, like SVM or Boosting, are generally mono-class, which introduces the problem of translating a global multi-class problem is several binary problems, while still being able to provide at the end an answer to the original multi-class issue. Present work aims at providing a solution to this multi-class problematic, by introducing a complete framework with a strong probabilistic and structured basis. It includes the study of error correcting output codes correlated with the definition of an optimal subdivision of the multi-class issue in several binary problems, in a complete automatic way. Machine learning algorithms are studied and benchmarked to facilitate and justify the final selection. Coupling of automatically calibrated classifiers output is obtained by applying iterative constrained regularisations, and a logical temporal fusion is applied on temporal-redundant data (like tracked vehicles) to enhance performances. Finally, ranking scores are computed to optimize precision and recall is ranking-based systems. Each step of the previously described system has been analysed from a theoretical an empirical point of view and new contributions are introduced, so as to obtain a complete mathematically coherent framework which is both generic and easy-to-use, as the learning procedure is almost completely automatic. On top of that, quantitative evaluations on two completely different datasets have assessed both the exactitude of previous assertions and the improvements that were achieved compared to previous methods.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.publisherÉcole nationale supérieure des télécommunications (França)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes
dc.subject.lcshPattern recognition systems
dc.subject.lcshMachine learning
dc.subject.otherprobabilistic framework
dc.subject.otherfusion
dc.subject.othermulticlass
dc.subject.othercalibration
dc.subject.othererror correcting output codes (ECOC)
dc.subject.othercoupling
dc.subject.otherlogical f usion
dc.subject.otherSVM
dc.subject.otherHTM Numenta
dc.titleMulti-class Classification with Machine Learning and Fusion
dc.typeMaster thesis (pre-Bologna period)
dc.subject.lemacReconeixement de formes (Informàtica)
dc.subject.lemacAprenentatge automàtic
dc.rights.accessOpen Access
dc.audience.educationlevelEstudis de primer/segon cicle
dc.audience.mediatorEscola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona
dc.audience.degreeENGINYERIA DE TELECOMUNICACIÓ (Pla 1992)


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