{"id":14377,"date":"2001-11-12T00:00:00","date_gmt":"2001-11-12T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/on-unsupervised-learning-of-bayesian-networks-and-conditional-gaussian-networks\/"},"modified":"2001-11-12T00:00:00","modified_gmt":"2001-11-12T00:00:00","slug":"on-unsupervised-learning-of-bayesian-networks-and-conditional-gaussian-networks","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/on-unsupervised-learning-of-bayesian-networks-and-conditional-gaussian-networks\/","title":{"rendered":"On unsupervised learning of bayesian networks and conditional gaussian networks"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Jos\u00e9 Manuel Pe\u00f1a Palomar <\/strong><\/h2>\n<p>Uno de los problemas que est\u00e1 presente en una gran variedad de campos, incluyendo reconocimiento de patrones, aprendizaje autom\u00e1tico, y estad\u00edstica, es el problema de la clasificaci\u00f3n no supervisada o aprendizaje no supervisado. dada una base de datos consistente en un conjunto de instancias con una estructura de grupo subyacente, la clasificaci\u00f3n no supervisada puede definirse como la descripci\u00f3n de la estructura de grupo subyacente cuando no se desconoce el grupo de pertenencia de cada instancia. La tesis se centra en el estudio de la clasificaci\u00f3n no supervisada desde la perspectiva probabil\u00edstica y, por tanto, no pretende obtener como soluci\u00f3n una participaci\u00f3n de la base de datos dada sino un modelo del mecanismo generador de los datos, esto es, una distribuci\u00f3n de probabilidad conjunta sobre las variables aleatorias del dominio.  un ejemplo cl\u00e1sico de aproximaci\u00f3n probabil\u00edstica al problema de la clasificaci\u00f3n no supervisada son los modelos finitos mixtos. Los cuales, aun reconociendo sus s\u00f3lidas bases estad\u00edsticas, conllevan una serie de dificultades que, en muchos casos, merman su practicidad y atractivo. Dichas dificultades tienen un origen com\u00f3n: las manipulaciones y operaciones en espacios altamente multidimensionales que los modelos finitos mixtos conllevan. la tesis aprovecha las ventajas de los modelos gr\u00e1ficos probabil\u00edsticos (principalmente manejabilidad, interpretabilidad, y modularidad), como paradigma que permite codificar distribuciones de probabilidad conjunta a la vez que evita los inconvenientes de los modelos finitos mixtos, con el fin de resolver el problema de la clasificaci\u00f3n no supervisada en dominios tanto discretos como continuos. En concreto, la tesis demuestra la utilidad de las redes bayesianas para clasificaci\u00f3n no supervisada en dominios discretos y de las redes condicionales gaussianas para clasificaci\u00f3n no supervisada en dominios continuos, como clases particulares de modelos gr\u00e1<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>On unsupervised learning of bayesian networks and conditional gaussian networks<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 On unsupervised learning of bayesian networks and conditional gaussian networks <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Jos\u00e9 Manuel Pe\u00f1a Palomar <\/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 11\/12\/2001<\/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:  Torrealdea folgado Francisco javier <\/li>\n<li>enrique Castillo ron (vocal)<\/li>\n<li>serafin Moral callejon (vocal)<\/li>\n<li>pedro Isasi vi\u00f1uela (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Jos\u00e9 Manuel Pe\u00f1a Palomar Uno de los problemas que est\u00e1 presente en una gran variedad de campos, [&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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