A Note on Probability and Conditional Probability SVM models

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dc.contributor.authorCarrasco, Miguel
dc.contributor.authorLópez, Julio
dc.contributor.authorIvorra, Benjamín Pierre Paul
dc.contributor.authorRamos Del Olmo, Ángel Manuel
dc.date.accessioned2024-07-18T09:19:14Z
dc.date.available2024-07-18T09:19:14Z
dc.date.issued2024-07
dc.description.abstractSupport Vector Machines (SVMs) are powerful tools in machine learning, widely used for classification and regression tasks. Over time, various extensions, such as Probability Estimation SVM (PSVM) and Conditional Probability SVM (CPSVM), have been proposed to enhance SVM performance across different conditions and datasets. This article offers a comprehensive review and analysis of SVMs, with a particular focus on the PSVM and CPSVM models. We delve into the intricacies of these models, addressing computational nuances and presenting corrected formulations where necessary. Our empirical evaluation, conducted on diverse benchmark datasets, implements both linear and nonlinear versions of these SVMs. Performance is benchmarked using the Balanced Accuracy metric. The results highlight the comparative strengths of these models in handling varied datasets and their potential advantages over traditional SVM formulations. To rigorously assess and compare the performance of these SVM variants, we employ statistical tests, including the Friedman test and post hoc analysis.
dc.description.departmentDepto. de Análisis Matemático y Matemática Aplicada
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedFALSE
dc.description.statusunpub
dc.identifier.urihttps://hdl.handle.net/20.500.14352/106845
dc.language.isoeng
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-106337GB-I00/ES/MODELIZACION, SIMULACION NUMERICA Y OPTIMIZACION PARA VARIOS PROBLEMAS DE INTERES GENERAL/
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.keywordSupport Vector Machines; Probability Support Vector Machine; Conditional Probability Support Vector Machine
dc.subject.ucmInvestigación operativa (Matemáticas)
dc.subject.unesco1207 Investigación Operativa
dc.titleA Note on Probability and Conditional Probability SVM models
dc.typejournal article
dspace.entity.typePublication
relation.isAuthorOfPublication6d5e1204-9b8a-40f4-b149-02d32e0bbed2
relation.isAuthorOfPublication581c3cdf-f1ce-41e0-ac1e-c32b110407b1
relation.isAuthorOfPublication.latestForDiscovery6d5e1204-9b8a-40f4-b149-02d32e0bbed2
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