{"id":87986,"date":"2018-03-10T00:12:50","date_gmt":"2018-03-10T00:12:50","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/maquina-de-vectores-soporte-adaptativa-y-compacta\/"},"modified":"2018-03-10T00:12:50","modified_gmt":"2018-03-10T00:12:50","slug":"maquina-de-vectores-soporte-adaptativa-y-compacta","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/maquina-de-vectores-soporte-adaptativa-y-compacta\/","title":{"rendered":"M\u00e1quina de vectores soporte adaptativa y compacta"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Fernando P\u00e9rez Cruz <\/strong><\/h2>\n<p>La m\u00e1quina de vectores soporte (svm) en un sistema de aprendizaje novedoso para construir clasificadores y funciones de regresi\u00f3n lineales y no lineales. la svm es una t\u00e9cnica no param\u00e9trica que construye la soluci\u00f3n de forma expl\u00edcita mediante una combinaci\u00f3n lineal de las muestras de entrenamiento. la caracter\u00edstica m\u00e1s relevante de la svm es su capacidad para resolver problemas en los que los datos son de gran dimensionalidad sin degradar la soluci\u00f3n por la falta de \u00e9stos. Esta propiedad la obtiene la svm definiendo y maximizando la distancia entra la frontera de clasificaci\u00f3n y las muestras, conocida como el margen. El funcional que debe minimizar la svm es convexo con restricciones lineales, por ello su soluci\u00f3n es \u00fanica, sin m\u00ednimos locales.  la svm presenta ciertas limitaciones en su formulaci\u00f3n original, como estar limitado a operar con muestras independientes e id\u00e9nticamente distribuidas (i.I.D.) O a una funci\u00f3n de coste fija. Adem\u00e1s su procedimiento de optimizaci\u00f3n es dif\u00edcil de implementar y presenta alta carga computacional, que limita su uso para problemas con muchas muestras. Por ello, la svm no se puede aplicar en la diversidad de aplicaciones de procesado de se\u00f1ales no estacionarios, en las que sus propiedades puedan ser deseables.  en los problemas de clasificaci\u00f3n o regresi\u00f3n la funci\u00f3n de coste puede venir determinada por el problema de resolver. El cambio de la funci\u00f3n de coste debe proporcionar soluciones de menor error que las alcanzadas con la svm y su funci\u00f3n de coste original.  para superar las limitaciones indicadas sobre la svm original, se ha tenido que modificar su procedimiento de optimizaci\u00f3n, que normalmente emplea programaci\u00f3n cuadr\u00e1tica, por uno de m\u00ednimos cuadrado ponderado e iterados (irwls). Este procedimiento es de por s\u00ed m\u00e1s veloz que los de programaci\u00f3n cuadr\u00e1tica, reduciendo considerablemente la carga computacional asociada a la resoluci\u00f3n de la svm.  en el t<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>M\u00e1quina de vectores soporte adaptativa y compacta<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 M\u00e1quina de vectores soporte adaptativa y compacta <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Fernando P\u00e9rez Cruz <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Polit\u00e9cnica de Madrid<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 15\/12\/2000<\/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>Antonio Artes Rodriguez<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: Miguel Calvo ram\u00f3n <\/li>\n<li>Miguel angel Lagunas hern\u00e1ndez (vocal)<\/li>\n<li>an\u00edbal ram\u00f3n Figueiras vidal (vocal)<\/li>\n<li>domingo Docampo amoedo (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Fernando P\u00e9rez Cruz La m\u00e1quina de vectores soporte (svm) en un sistema de aprendizaje novedoso para construir [&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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