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Analyzing sensory data using non-linear preference learning with feature subset selection

dc.contributor.authorLuaces Rodríguez
dc.contributor.authorFernández Bayón, Gustavo 
dc.contributor.authorQuevedo Pérez, José Ramón 
dc.contributor.authorDíez Peláez, Jorge 
dc.contributor.authorCoz Velasco, Juan José del 
dc.contributor.authorBahamonde Rionda, Antonio 
dc.date.accessioned2015-06-16T08:32:54Z
dc.date.available2015-06-16T08:32:54Z
dc.date.issued2004
dc.identifier.isbn978-3-540-23105-9
dc.identifier.urihttp://hdl.handle.net/10651/31232
dc.description15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004spa
dc.description.abstractThe quality of food can be assessed from different points of view. In this paper, we deal with those aspects that can be appreciated through sensory impressions. When we are aiming to induce a function that maps object descriptions into ratings, we must consider that consumers’ ratings are just a way to express their preferences about the products presented in the same testing session. Therefore, we postulate to learn from consumers’ preference judgments instead of using an approach based on regression. This requires the use of special purpose kernels and feature subset selection methods. We illustrate the benefits of our approach in two families of real-world data basesspa
dc.format.extentp. 286-297spa
dc.language.isoengspa
dc.publisherSpringerspa
dc.relation.ispartofMachine Learning: ECML 2004spa
dc.rightsCC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleAnalyzing sensory data using non-linear preference learning with feature subset selectionspa
dc.typeinfo:eu-repo/semantics/conferenceObjectspa
dc.identifier.doi10.1007/978-3-540-30115-8_28
dc.type.dcmitextspa
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-540-30115-8_28spa
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessspa


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