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dc.contributor.authorRodriguez, Saithspa
dc.contributor.authorPérez, Katherínspa
dc.contributor.authorQuintero, Carlosspa
dc.contributor.authorLópez, Jorgespa
dc.contributor.authorRojas, Eyberthspa
dc.contributor.authorCalderón, Juanspa
dc.date.accessioned2019-12-17T16:11:45Zspa
dc.date.available2019-12-17T16:11:45Zspa
dc.date.issued2015-12-15spa
dc.identifier.urihttp://hdl.handle.net/11634/20409spa
dc.description.abstractIn this paper we propose a methodology to build multiclass classifiers for the human-robot interaction problem. Our solution uses kernel-based classifiers and assumes that each data type is better represented by a different kernel. The kernels are then combined into one single kernel that uses all the dataset involved in the HRI process. The results on real data shows that our proposal is capable of obtaining lower generalization errors due to the use of specific kernels for each data type. Also, we show that our proposal is more robust when presented to noise in either or both data types.spa
dc.format.mimetypeapplication/pdfspa
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 Colombia*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/co/*
dc.titleIdentification of multimodal human-robot interaction using combined kernelsspa
dc.typeGeneración de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicosspa
dc.subject.keywordHuman-robot interactionspa
dc.subject.keywordCombined Kernelsspa
dc.subject.keywordData typesspa
dc.subject.keywordNoisespa
dc.coverage.campusCRAI-USTA Bogotáspa
dc.identifier.doihttps://doi.org/10.1007/978-3-319-28031-8_23spa
dc.description.domainhttp://unidadinvestigacion.usta.edu.cospa
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