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dc.contributor.author | Salazar Afanador, Addisson | es_ES |
dc.contributor.author | Vergara Domínguez, Luís | es_ES |
dc.contributor.author | Vidal, Enrique | es_ES |
dc.date.accessioned | 2023-09-22T18:01:58Z | |
dc.date.available | 2023-09-22T18:01:58Z | |
dc.date.issued | 2023-04 | es_ES |
dc.identifier.issn | 0031-3203 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/196980 | |
dc.description.abstract | [EN] In this paper, a theoretical learning curve is derived for the multi-class Bayes classifier. This curve fits general multivariate parametric models of the class-conditional probability density. The derivation uses a proxy approach based on analyzing the convergence of a statistic which is proportional to the posterior probability of the true class. By doing so, the curve depends only on the training set size and on the dimension of the feature vector; it does not depend on the model parameters. Essentially, the learning curve provides an estimate of the reduction in the excess of the probability of error that can be obtained by increasing the training set size. This makes it attractive in order to deal with the practical problems of defining appropriate training set sizes. | es_ES |
dc.description.sponsorship | Acknowledgments Grant TEC2017-84743-P funded by MCIN/AEI/10.13039/50110 0 011033 and by the European Union. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Pattern Recognition | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Classification | es_ES |
dc.subject | Parameter learning | es_ES |
dc.subject | Sample size | es_ES |
dc.subject | Training set size | es_ES |
dc.subject | Probability of error | es_ES |
dc.subject.classification | TEORÍA DE LA SEÑAL Y COMUNICACIONES | es_ES |
dc.title | A proxy learning curve for the Bayes classifier | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.patcog.2022.109240 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2017-84743-P/ES/METODOS INFORMADOS PARA LA SINTESIS DE SEÑALES/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.date.embargoEndDate | 2025-04-30 | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació | es_ES |
dc.description.bibliographicCitation | Salazar Afanador, A.; Vergara Domínguez, L.; Vidal, E. (2023). A proxy learning curve for the Bayes classifier. Pattern Recognition. 136:1-14. https://doi.org/10.1016/j.patcog.2022.109240 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.patcog.2022.109240 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 14 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 136 | es_ES |
dc.relation.pasarela | S\488209 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | Universitat Politècnica de València |