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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.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/MEC//CSD2007-00018/ES/Multimodal Intraction in Pattern Recognition and Computer Visionm/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/FP7/216886/EU/Pattern Analysis, Statistical Modelling and Computational Learning 2/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//TIN2009-14511/ES/Traduccion De Textos Y Transcripcion De Voz Interactivas/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//TIN2009-14633-C03-01/ES/Multimodal Interaction For Text Transcription With Adaptive Learning/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MITURCO//TSI-020110-2009-0439/ES/ERUDITO.COM/ | 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 |
dc.contributor.funder | Ministerio de Educación y Ciencia | es_ES |