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How to learn consumer preferences from the analysis of sensory data by means of support vector machines

dc.contributor.authorBahamonde Rionda, Antonio 
dc.contributor.authorDíez Peláez, Jorge 
dc.contributor.authorQuevedo Pérez, José Ramón 
dc.contributor.authorLuaces Rodríguez, Óscar 
dc.contributor.authorCoz Velasco, Juan José del 
dc.date.accessioned2015-04-14T07:05:10Z
dc.date.available2015-04-14T07:05:10Z
dc.date.issued2007
dc.identifier.citationTrends in Food Science & Technology, 18(1), p. 20-28 (2007); doi:10.1016/j.tifs.2006.07.014
dc.identifier.issn0924-2244
dc.identifier.urihttp://hdl.handle.net/10651/30619
dc.description.abstractIn this paper we discuss how to model preferences from a collection of ratings provided by a panel of consumers of some kind of food product. We emphasize the role of tasting sessions, since the ratings tend to be relative to each session and hence regression methods are unable to capture consumer preferences. The method proposed is based on the use of Support Vector Machines (SVM) and provides both linear and nonlinear models. To illustrate the performance of the approach, we report the experimental results obtained with a couple of real world datasets
dc.format.extentp. 20-28spa
dc.language.isoengspa
dc.publisherElsevier
dc.relation.ispartofTrends in Food Science & Technology, 18(1)spa
dc.rights© 2007 Elsevier
dc.titleHow to learn consumer preferences from the analysis of sensory data by means of support vector machinesspa
dc.typejournal article
dc.identifier.doi10.1016/j.tifs.2006.07.014
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.tifs.2006.07.014
dc.rights.accessRightsopen access


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