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Hierarchical classification using SVM
dc.contributor.author | Díez Peláez, Jorge | |
dc.contributor.author | Coz Velasco, Juan José del | |
dc.date.accessioned | 2016-03-18T08:54:54Z | |
dc.date.available | 2016-03-18T08:54:54Z | |
dc.date.issued | 2009-11 | |
dc.identifier.isbn | 978-84-692-6424-9 | |
dc.identifier.uri | http://hdl.handle.net/10651/35745 | |
dc.description | XIII Conferencia de , XIII Conferencia de la Asociación Española para la Inteligencia Artificial, CAEPIA–TTIA 2009, Sevilla, 9–13 de Noviembre de 2009 | spa |
dc.description.abstract | In hierarchical classi cations classes are arranged in a hierar- chy represented by a tree forest, and each example is labeled with a set of classes located in paths from roots to leaves or internal nodes. In other words, both multiple and partial paths are allowed. A straightforward approach to learn these classi ers consists in learning one binary classi- er per node of the hierarchy; the hierarchical classi er is then obtained using a top-down evaluation procedure. In this paper, we present a new approach where node classi ers are learned by binary SVMs weighted according to the hierarchy structure and the loss function used to mea- sure the goodness of the classi ers. The result is a collection of modular algorithms that are competitive with state-of-the-art approaches. More- over, the bene ts of the modularity include the possibility of parallel implementations, and the use of all available and well-known techniques to tune binary classi cation SVMs | spa |
dc.description.sponsorship | The research reported in this paper has been supported in part under Spanish Ministerio de Educaci on y Ciencia (MEC) grant TIN2008-06247 | spa |
dc.format.extent | p. 359-368 | spa |
dc.language.iso | eng | spa |
dc.publisher | Asociación Española para la Inteligencia Artificial | spa |
dc.relation.ispartof | Actas de la XIII Conferencia de la Asociación Española para la Inteligencia Artificial, CAEPIA – TTIA 2009 | spa |
dc.rights | © 2009 Asociación Española para la Inteligencia Artificial | |
dc.title | Hierarchical classification using SVM | spa |
dc.type | book part | spa |
dc.relation.projectID | MEC/TIN2008-06247 | spa |
dc.rights.accessRights | open access | spa |
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