{"id":63663,"date":"2018-03-09T22:51:36","date_gmt":"2018-03-09T22:51:36","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/optimization-based-methods-for-classification-and-regression-problems-with-imprecise-data\/"},"modified":"2018-03-09T22:51:36","modified_gmt":"2018-03-09T22:51:36","slug":"optimization-based-methods-for-classification-and-regression-problems-with-imprecise-data","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/estadistica\/optimization-based-methods-for-classification-and-regression-problems-with-imprecise-data\/","title":{"rendered":"Optimization-based methods for classification and regression problems with imprecise data"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Jos\u00e9 Feliciano Gordillo Santofimia <\/strong><\/h2>\n<p>In this thesis, we develop several tools to solve classification and regression problems where the elements of the database are sets affected by some kind of uncertainty several particular cases are studied via this methodology, interval data. Perturbed data and bags of instances. the classifier and regressor is defined by following the strategy successfully used in support vector machines of maximizing the margin in each case, an optimization problem is formulated which must be solved to obtain an optimal classifier or regressor. these techniques are shown to be useful for imputation based on intervals and for a semi-obnoxious location problem (in the framework of locational  analysis).<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Optimization-based methods for classification and regression problems with imprecise data<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Optimization-based methods for classification and regression problems with imprecise data <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Jos\u00e9 Feliciano Gordillo Santofimia <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Sevilla<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 28\/03\/2008<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Emilio Carrizosa Priego<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: eduardo Conde sanchez <\/li>\n<li>belen Martin barragan (vocal)<\/li>\n<li>bernard Menderick (vocal)<\/li>\n<li>edgard Nyssen (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Jos\u00e9 Feliciano Gordillo Santofimia In this thesis, we develop several tools to solve classification and regression problems [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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