{"id":80856,"date":"1999-08-10T00:00:00","date_gmt":"1999-08-10T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/un-modelo-de-aprendizaje-e-inferencia-a-partir-de-informacion-imperfecta\/"},"modified":"1999-08-10T00:00:00","modified_gmt":"1999-08-10T00:00:00","slug":"un-modelo-de-aprendizaje-e-inferencia-a-partir-de-informacion-imperfecta","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/un-modelo-de-aprendizaje-e-inferencia-a-partir-de-informacion-imperfecta\/","title":{"rendered":"Un modelo de aprendizaje e inferencia a partir de informacion imperfecta"},"content":{"rendered":"<h2>Tesis doctoral de <strong> M. Carmen Garrido Carrera <\/strong><\/h2>\n<p>En este trabajo se analiza la informaci\u00f3n imperfecta en el contexto de los mecanismos autom\u00e1ticos de inferencia y aprendizaje inductivos.  se presenta el modelo mfgn( mixtures of factorized generalized normals) como un m\u00e9todo eficiente para realizar inferencia y aprendizaje desde informaci\u00f3n imperfecta.  el modelo obtiene una expresi\u00f3n explicita de la funci\u00f3n de densidad conjunta modelo-observaci\u00f3n, donde tanto la densidad conjunta como la informaci\u00f3n de entrada pueden ser interpretadas como funciones de masas que se combinan.  la estructura matem\u00e1tica de mezcla de normales generalizadas factorizadas, permite una computaci\u00f3n eficiente de las densidades a posteriori, as\u00ed como que la interpretaci\u00f3n del modelo como funci\u00f3n de masas d\u00e9 lugar a evidencias definidas sobre dominios de una sola variable, lo cual facilita el trabajo con la regla de evidencias de dempster-shafer.  el modelo permite,tanto en su fase de inferencia como de aprendizaje, utilizar informaci\u00f3n expresada de una gran variedad de formas a trav\u00e9s de modelos de incertidumbre objetiva, subjetiva e imprecisi\u00f3n.  los resultados experimentales indican que el modelo mfgn es capaz de reconstruir el modelo de dependencias de los atributos y realizar inferencia a partir de observaciones con un grado moderadamente alto de incertidumbre e imprecisi\u00f3n.  el hecho de situar el modelo en el marco de una teor\u00eda m\u00e1s general, como es la teor\u00eda de evidencias de demspter-shafer, permitir\u00e1 usar distintas interpretaciones de la informaci\u00f3n imperfecta seg\u00fan la forma que parezca m\u00e1s adecuada.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Un modelo de aprendizaje e inferencia a partir de informacion imperfecta<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Un modelo de aprendizaje e inferencia a partir de informacion imperfecta <\/li>\n<li><strong>Autor:<\/strong>\u00a0 M. Carmen Garrido Carrera <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Murcia<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 08\/10\/1999<\/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>Jos\u00e9 Manuel Cadenas Figueredo<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: fernando Martin rubio <\/li>\n<li>pablo Bustos garcia de castro (vocal)<\/li>\n<li>Jos\u00e9 Luis Verdegay galdeano (vocal)<\/li>\n<li>felix Monasterio-huelin macia (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de M. Carmen Garrido Carrera En este trabajo se analiza la informaci\u00f3n imperfecta en el contexto de los [&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 center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[1890,2528,126,8235],"tags":[80241,4013,3604,92235,172869,49575],"class_list":["post-80856","post","type-post","status-publish","format-standard","hentry","category-ciencia-de-los-ordenadores","category-inteligencia-artificial","category-matematicas","category-murcia","tag-felix-monasterio-huelin-macia","tag-fernando-martin-rubio","tag-jose-luis-verdegay-galdeano","tag-jose-manuel-cadenas-figueredo","tag-m-carmen-garrido-carrera","tag-pablo-bustos-garcia-de-castro"],"_links":{"self":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/80856","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/comments?post=80856"}],"version-history":[{"count":0,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/80856\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/media?parent=80856"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/categories?post=80856"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/tags?post=80856"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}