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dc.contributor.author | Paredes Palacios, Roberto | es_ES |
dc.contributor.author | Girolami, Mark | es_ES |
dc.date.accessioned | 2014-03-28T09:56:48Z | |
dc.date.issued | 2011 | |
dc.identifier.isbn | 978-3-642-21256-7 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://hdl.handle.net/10251/36702 | |
dc.description.abstract | A probabilistic k-nn (PKnn) method was introduced in [13] under the Bayesian point of view. This work showed that posterior inference over the parameter k can be performed in a relatively straightforward manner using Markov Chain Monte Carlo (MCMC) methods. This method was extended by Everson and Fieldsen [14] to deal with metric learning. In this work we propose two different dissimilarities functions to be used inside this PKnn framework. These dissimilarities functions can be seen as a simplified version of the full-covariance distance functions just proposed. Furthermore we propose to use a class- dependent dissimilarity function as proposed in [8] aim at improving the k-nn classifier. In the present work we pursue a simultaneously learning of the dissimilarity function parameters together with the parameter k of the k-nn classifier. The experiments show that this simultaneous learning lead to an improvement of the classifier with respect to the standard k-nn and state-of-the-art technique as well. | es_ES |
dc.description.sponsorship | Work supported by the Spanish MEC/MICINN under the MIPRCV Consolider Ingenio 2010 program (CSD2007-00018). | es_ES |
dc.format.extent | 8 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Springer Verlag (Germany) | es_ES |
dc.relation.ispartof | Pattern Recognition and Image Analysis | es_ES |
dc.relation.ispartofseries | Lecture Notes in Computer Science;vol. 6669 | |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Statistical Pattern-Recognition | es_ES |
dc.subject | Classification | es_ES |
dc.subject | Metrics | es_ES |
dc.subject | Rules | es_ES |
dc.subject | Error | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | On the use of diagonal and class-dependent weighted distances for the Probabilistic k-nearest neighbor | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.embargo.lift | 10000-01-01 | |
dc.embargo.terms | forever | es_ES |
dc.identifier.doi | 10.1007/978-3-642-21257-4_33 | |
dc.relation.projectID | info:eu-repo/grantAgreement/MEC//CSD2007-00018/ES/Multimodal Intraction in Pattern Recognition and Computer Visionm/ | es_ES |
dc.rights.accessRights | Cerrado | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario Mixto Tecnológico de Informática - Institut Universitari Mixt Tecnològic d'Informàtica | es_ES |
dc.description.bibliographicCitation | Paredes Palacios, R.; Girolami, M. (2011). On the use of diagonal and class-dependent weighted distances for the Probabilistic k-nearest neighbor. En Pattern Recognition and Image Analysis. Springer Verlag (Germany). 6669:265-272. https://doi.org/10.1007/978-3-642-21257-4_33 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.conferencename | 5th Iberian Conference, IbPRIA 2011 | es_ES |
dc.relation.conferencedate | June 8-10, 2011 | es_ES |
dc.relation.conferenceplace | Las Palmas de Gran Canaria, Spain | es_ES |
dc.relation.publisherversion | http://link.springer.com/chapter/10.1007/978-3-642-21257-4_33 | es_ES |
dc.description.upvformatpinicio | 265 | es_ES |
dc.description.upvformatpfin | 272 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 6669 | es_ES |
dc.relation.senia | 218725 | |
dc.contributor.funder | Ministerio de Educación y Ciencia | es_ES |
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