{"id":75797,"date":"2018-03-09T23:21:06","date_gmt":"2018-03-09T23:21:06","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/learning-bayesian-networks-form-data-with-factorisation-and-classification-purposes-applications-in-biomedicine\/"},"modified":"2018-03-09T23:21:06","modified_gmt":"2018-03-09T23:21:06","slug":"learning-bayesian-networks-form-data-with-factorisation-and-classification-purposes-applications-in-biomedicine","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/learning-bayesian-networks-form-data-with-factorisation-and-classification-purposes-applications-in-biomedicine\/","title":{"rendered":"Learning bayesian networks form data with factorisation and classification purposes. applications in biomedicine"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Rosa Blanco G\u00f3mez <\/strong><\/h2>\n<p>El trabajo de la tesis realiza aportaciones en dos \u00e1reas relacionadas: el aprendizaje autom\u00e1tico desde datos de redes baysianas y los clasificadores bayesianos.  en el aprendizaje de redes bayesianas, las aproximaciones de \u00abscore+search\u00bb buscan la mejor red bayesiana para una medida y un espacio de b\u00fasqueda dados. En este campo, las aportaciones de la tesis se centran en el uso de los m\u00e9todos floating, el grasp y los algoritmos de estimaci\u00f3n de distribuciones como motores en la b\u00fasqueda de redes bayesianas. Aunque los resultados obtenidos no son tan buenos como se esperaba, s\u00ed son competitivos con los obtenidos por otros algoritmos propuestos por la literatura.  la clasificaci\u00f3n supervisada consiste en construir un modelo a partir de un conjunto de d atos etiquetados, para que en el futuro ese modelo prediga la clase de una instancia sin etiquetar. Relacionado con la clasificaci\u00f3n supervisada se encuentra la selecci\u00f3n de variables, donde s\u00f3lo de eligen las variables que aportan informaci\u00f3n para la clase. En esta tesis, los paradigmas de clasificaci\u00f3n elegidos son un subconjunto de clasificadores bayesianos; naive bayes, semi naive bayes, tree augmented naive bayes and k dependence bayesian classifier. Las aportaciones en esta \u00e1rea se centran an la propuesta de aproximaciones de filtrado y envoltura para estos clasificadores. de la experimentaci\u00f3n realizada se puede apreciar que los clasificadores bayesianos propuestos mejoran el resultado del naive bayes cuando los datos incluyen variables redundantes e irrelevantes.  finalmente, se han aplicado las aproximaciones de filtrado y envoltura para los clasificadores bayesianos a tres problemas reales de entornos biom\u00e9dicos, obteniendo resultados muy prometedores.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Learning bayesian networks form data with factorisation and classification purposes. applications in biomedicine<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Learning bayesian networks form data with factorisation and classification purposes. applications in biomedicine <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Rosa Blanco G\u00f3mez <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Pa\u00eds vasco\/euskal herriko unibertsitatea<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 15\/07\/2005<\/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>Pedro Larra\u00f1aga Mugica<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: ram\u00f3n L\u00f3pez de m\u00e1ntaras bad\u00eda <\/li>\n<li>seraf\u00edn Moral (vocal)<\/li>\n<li>joao Gama (vocal)<\/li>\n<li> Campos Luis Miguel de (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Rosa Blanco G\u00f3mez El trabajo de la tesis realiza aportaciones en dos \u00e1reas relacionadas: el aprendizaje autom\u00e1tico [&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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