{"id":68518,"date":"2008-05-12T00:00:00","date_gmt":"2008-05-12T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/bridging-the-gap-between-distance-and-generalisation-symbolic-learning-in-metric-spaces\/"},"modified":"2008-05-12T00:00:00","modified_gmt":"2008-05-12T00:00:00","slug":"bridging-the-gap-between-distance-and-generalisation-symbolic-learning-in-metric-spaces","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/bridging-the-gap-between-distance-and-generalisation-symbolic-learning-in-metric-spaces\/","title":{"rendered":"Bridging the gap between distance and generalisation. symbolic learning in metric spaces."},"content":{"rendered":"<h2>Tesis doctoral de <strong> Vicente Estruch Gregori <\/strong><\/h2>\n<p>El aprendizaje autom\u00e1tico es la disciplina que se centra en el dise\u00f1o de algoritmos que pueden aprender a partir de datos recogidos de la experiencia. Muchos de estos algoritmos son fundamentales en aplicaciones tales como miner\u00eda de datos, ling\u00ed\u00bc\u00edstica computacional, visi\u00f3n artificial, etc. Sin embargo, a pesar de la aplicabilidad de estos algoritmos y su alto rendimiento mostrado en problemas reales, nos encontramos con la situaci\u00f3n que muchos de ellos est\u00e1n dise\u00f1ados para contextos muy particulares. Esto equivale a decir que los algoritmos existentes deben ser redise\u00f1ados o que nuevos algoritmos deben ser inventados cada vez que el contexto de aprendizaje var\u00ede. Por este motivo, existe un gran inter\u00e9s en desarrollar m\u00e9todos de prop\u00f3sito general que puedan ser ejecutados o sistem\u00e1ticamente adaptados a diferentes escenarios de aprendizaje. Uno de estos escenarios hace referencia a la representaci\u00f3n de los datos. En t\u00e9rminos generales, los datos o ejemplos recogidos se representan mediante tuplas de componentes num\u00e9ricas y\/o categ\u00f3ricas (representaci\u00f3n proposicional de los datos) o usando otros formalismos m\u00e1s complejos pero a su vez m\u00e1s expresivos que involucran estructuras tales como conjuntos, secuencias, grafos, etc. (Datos estructurados). As\u00ed pues, el objetivo principal es obtener algoritmos de aprendizaje cuyo dise\u00f1o sea lo m\u00e1s independiente posible de la representaci\u00f3n de los datos que haya sido utilizada. En la l\u00ednea de dise\u00f1ar m\u00e9todos de aprendizaje que puedan tratar con o adaptarse a diferentes representaciones de los datos, encontramos en la literatura diferentes aproximaciones que se ubican principalmente en dos grupos diferentes: los llamados m\u00e9todos basados en distancias (o de forma m\u00e1s gen\u00e9rica, m\u00e9todos basados en similitud) y los m\u00e9todos simb\u00f3licos. Cada uno de estos grupos de m\u00e9todos tiene sus propias ventajas e inconvenientes. Los m\u00e9todos basados en distancias pueden tratar con cualquier representaci\u00f3n de los datos.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Bridging the gap between distance and generalisation. symbolic learning in metric spaces.<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Bridging the gap between distance and generalisation. symbolic learning in metric spaces. <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Vicente Estruch Gregori <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Polit\u00e9cnica de Valencia<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 05\/12\/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>Jose Hernandez Orallo<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: Mar\u00eda Alpuente frasnedo <\/li>\n<li>Mar\u00eda  purificaci\u00f3n Cari\u00f1ena amigo (vocal)<\/li>\n<li>peter Flach (vocal)<\/li>\n<li>nicolas Lachiche (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Vicente Estruch Gregori El aprendizaje autom\u00e1tico es la disciplina que se centra en el dise\u00f1o de algoritmos [&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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