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Discriminative Bernoulli Mixture Models for Handwritten Digit Recognition

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Discriminative Bernoulli Mixture Models for Handwritten Digit Recognition

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dc.contributor.author Giménez Pastor, Adrián es_ES
dc.contributor.author Andrés Ferrer, Jesús es_ES
dc.contributor.author Juan Císcar, Alfonso es_ES
dc.contributor.author Serrano Martinez Santos, Nicolas es_ES
dc.date.accessioned 2015-05-13T09:44:14Z
dc.date.available 2015-05-13T09:44:14Z
dc.date.issued 2011-09-18
dc.identifier.isbn 978-0-7695-4520-2
dc.identifier.isbn 978-1-4577-1350-7
dc.identifier.issn 1520-5363
dc.identifier.uri http://hdl.handle.net/10251/50140
dc.description.abstract Bernoulli-based models such as Bernoulli mixtures or Bernoulli HMMs (BHMMs), have been successfully applied to several handwritten text recognition (HTR) tasks which range from character recognition to continuous and isolated handwritten words. All these models belong to the generative model family and, hence, are usually trained by (joint) maximum likelihood estimation (MLE). Despite the good properties of the MLE criterion, there are better training criteria such as maximum mutual information (MMI). The MMI is a widespread criterion that is mainly employed to train discriminative models such as log-linear (or maximum entropy) models. Inspired by the Bernoulli mixture classifier, in this work a log-linear model for binary data is proposed, the so-called mixture of multiclass logistic regression. The proposed model is proved to be equivalent to the Bernoulli mixture classifier. In this way, we give a discriminative training framework for Bernoulli mixture models. The proposed discriminative training framework is applied to a well-known Indian digit recognition task. es_ES
dc.description.sponsorship Work supported by the EC (FEDER/FSE) and the Spanish MEC/MICINN under the MIPRCV “Consolider Ingenio 2010” program (CSD2007-00018), iTrans2 (TIN2009-14511) and MITTRAL (TIN2009-14633-C03-01) projects. Also supported by the IST Programme of the European Community, under the PASCAL2 Network of Excellence, IST-2007-216886, and by the Spanish MITyC under the erudito.com (TSI-020110-2009-439). es_ES
dc.language Inglés es_ES
dc.publisher Institute of Electrical and Electronics Engineers (IEEE) es_ES
dc.relation MICINN/CSD2007-00018 es_ES
dc.relation info:eu-repo/grantAgreement/MICINN//TIN2009-14511/ES/Traduccion De Textos Y Transcripcion De Voz Interactivas/ es_ES
dc.relation info:eu-repo/grantAgreement/MICINN//TIN2009-14633-C03-01/ES/Multimodal Interaction For Text Transcription With Adaptive Learning/ es_ES
dc.relation MITYC/TSI-020110-2009-439 es_ES
dc.relation.ispartof Document Analysis and Recognition (ICDAR), 2011 International Conference on es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Bernoulli mixture es_ES
dc.subject Discriminative training es_ES
dc.subject MMI es_ES
dc.subject Mixture of multi-class logistic regression es_ES
dc.subject Log-linear models es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Discriminative Bernoulli Mixture Models for Handwritten Digit Recognition es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1109/ICDAR.2011.118
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/216886/EU/Pattern Analysis, Statistical Modelling and Computational Learning 2/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Giménez Pastor, A.; Andrés Ferrer, J.; Juan Císcar, A.; Serrano Martinez Santos, N. (2011). Discriminative Bernoulli Mixture Models for Handwritten Digit Recognition. En Document Analysis and Recognition (ICDAR), 2011 International Conference on. Institute of Electrical and Electronics Engineers (IEEE). 558-562. https://doi.org/10.1109/ICDAR.2011.118 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1109/ICDAR.2011.118 es_ES
dc.description.upvformatpinicio 558 es_ES
dc.description.upvformatpfin 562 es_ES
dc.relation.senia 208344
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder European Social Fund es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Ministerio de Industria, Turismo y Comercio es_ES


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