Introducing the Discriminative Paraconsistent Machine (DPM)

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.creatorGuido, Rodrigo Capobianco [UNESP]
dc.creatorBarbon Junior, Sylvio
dc.creatorSolgon, Regiane Denise
dc.creatorPaulo, Kátia Cristina Silva
dc.creatorRodrigues, Luciene Cavalcanti
dc.creatorSilva, Ivan Nunes da
dc.creatorEscola, João Paulo Lemos
dc.date2015-04-27T11:56:02Z
dc.date2015-04-27T11:56:02Z
dc.date2013
dc.date.accessioned2023-09-12T04:46:28Z
dc.date.available2023-09-12T04:46:28Z
dc.identifierhttp://dx.doi.org/10.1016/j.ins.2012.09.028
dc.identifierInformation Sciences, n. 221, p. 389-402, 2013.
dc.identifier0020-0255
dc.identifierhttp://hdl.handle.net/11449/122786
dc.identifier6542086226808067
dc.identifier0000-0002-0924-8024
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8772233
dc.descriptionThis paper introduces a new tool for pattern recognition. Called the Discriminative Paraconsistent Machine (DPM), it is based on a supervised discriminative model training that incorporates paraconsistency criteria and allows an intelligent treatment of contradictions and uncertainties. DPMs can be applied to solve problems in many fields of science, using the tests and discussions presented here, which demonstrate their efficacy and usefulness. Major difficulties and challenges that were overcome consisted basically in establishing the proper model with which to represent the concept of paraconsistency.
dc.descriptionUniversidade Estadual Paulista Júlio de Mesquita Filho, Instituto de Biociências Letras e Ciências Exatas de São José do Rio Preto, Sao Jose do Rio Preto, Av. Cristovao Colombo, 2265, Jardim Nazareth, CEP 15054-000, SP, Brasil
dc.format389-402
dc.languageeng
dc.relationInformation Sciences
dc.relation4.305
dc.relation1,635
dc.rightsAcesso restrito
dc.sourceCurrículo Lattes
dc.subjectParaconsistency
dc.subjectPattern recognition
dc.subjectDiscriminative model training
dc.titleIntroducing the Discriminative Paraconsistent Machine (DPM)
dc.typeArtigo


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