Exploring some practical issues of SVM+: Is really privileged information that helps?
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Otros documentos de la autoría: Serra Toro, Carlos; Traver Roig, Vicente Javier; Pla, Filiberto
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http://dx.doi.org/10.1016/j.patrec.2014.01.013 |
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Título
Exploring some practical issues of SVM+: Is really privileged information that helps?Fecha de publicación
2014Editor
ElsevierISSN
0167-8655Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0167865514000270Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Learning using privileged information (LUPI) is a machine learning paradigm which aims at improving classification by taking advantage of information that is only available at training time —not at test time. SVM+ is ... [+]
Learning using privileged information (LUPI) is a machine learning paradigm which aims at improving classification by taking advantage of information that is only available at training time —not at test time. SVM+ is an SVM-based implementation of LUPI. Despite this paradigm has potential interest for many applications, both LUPI and SVM+ have been scarcely explored up to date. In this work we report our effort in reproducing some results in the SVM+ literature and explore some practical issues of SVM+. The main finding is that just using randomly generated features as privileged information may perform similarly to using sensible (i.e. meaningful a priori) privileged information, at least in some problems. [-]
Publicado en
Pattern Recognition Letters 42 (2014) 40–46Derechos de acceso
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