{"id":66710,"date":"2018-03-09T22:55:04","date_gmt":"2018-03-09T22:55:04","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/mult-feature-construction-based-on-genetic-algorithms-and-non-algebraic-feature-representation-to-facilitate-learning-concepts-with-complex-interactions\/"},"modified":"2018-03-09T22:55:04","modified_gmt":"2018-03-09T22:55:04","slug":"mult-feature-construction-based-on-genetic-algorithms-and-non-algebraic-feature-representation-to-facilitate-learning-concepts-with-complex-interactions","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/inteligencia-artificial\/mult-feature-construction-based-on-genetic-algorithms-and-non-algebraic-feature-representation-to-facilitate-learning-concepts-with-complex-interactions\/","title":{"rendered":"Mult-feature construction based on genetic algorithms and non-algebraic feature representation to facilitate learning concepts with complex interactions"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Leila Shila Shafti <\/strong><\/h2>\n<p>Los datos de problemas reales normalmente se han preparado para fines distintos a la miner\u00eda de datos y el aprendizaje autom\u00e1tico, y por lo tanto, se han representado por atributos primitivos. La representaci\u00f3n primitiva de datos facilita la existencia de interacci\u00f3n entre atributos cuya complejidad hace que la informaci\u00f3n relevante queda oculta para la mayor\u00eda de los sistemas de aprendizaje. La inducci\u00f3n constructiva (ic) se ha introducido para facilitar el aprendizaje mediante la reestructuraci\u00f3n de la representaci\u00f3n primitiva. Recientemente, se han obtenido muchos progresos en ic; sin embargo, los m\u00e9todos de aprendizaje todav\u00eda se enfrentan a graves dificultades cuando se aplican a conceptos con interacciones complejas. %&#038;\/Esta investigaci\u00f3n tiene como objetivo facilitar el aprendizaje de conceptos con interacciones complejas cuando el \u00fanico conocimiento disponible sobre el concepto sea la representaci\u00f3n primitiva de datos de entrenamiento. La tesis describe los principales requisitos funcionales que un m\u00e9todo de ic deber\u00e1 cumplir con el fin de afrontar este tipo de problemas y propone un nuevo marco de trabajo que descompone la tarea compleja de ic en tareas peque\u00f1as y m\u00e1s f\u00e1ciles. A parte de este marco y los requisitos funcionales se ha dise\u00f1ado dos m\u00e9todos, dci y mfe3\/ga. Las evaluaciones emp\u00edricas sobre problemas sint\u00e9ticos y problemas reales muestran la eficacia de estos m\u00e9todos para mejorar la precisi\u00f3n del aprendizaje. El marco propuesto se puede usar como un modelo para dise\u00f1ar una herramienta para ser integrada en una caja de herramientas de aprendizaje autom\u00e1tico con el fin de facilitar el aprendizaje de conceptos dif\u00edciles con datos primitivos.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Mult-feature construction based on genetic algorithms and non-algebraic feature representation to facilitate learning concepts with complex interactions<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Mult-feature construction based on genetic algorithms and non-algebraic feature representation to facilitate learning concepts with complex interactions <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Leila Shila Shafti <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Aut\u00f3noma de Madrid<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 29\/07\/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>Eduardo P\u00e9rez P\u00e9rez<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: manuel Alfonseca moreno <\/li>\n<li>basilio Sierra araujo (vocal)<\/li>\n<li>ricardo Aler mur (vocal)<\/li>\n<li>Carlos Cotta porras (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Leila Shila Shafti Los datos de problemas reales normalmente se han preparado para fines distintos a la [&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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