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dc.date.accessioned 2015-03-27T16:53:42Z
dc.date.available 2015-03-27T16:53:42Z
dc.date.issued 2015-04
dc.identifier.uri http://hdl.handle.net/10915/44718
dc.description.abstract The early detection abnormal fermentations (sluggish and stuck) is one of the main problems that appear in wine production, due to the signi cant impacts in wine quality and utility. This situation is specially important in Chile, which is one of the top ten worldwide wine production countries. In last years, two di erent methods coming from Computational Intelligence have been applied to solve this problem: Arti cial Neural Networks and Support Vector Machines. In this work we present the main results that have been obtained to detect abnormal wine fermentations applying these approaches. The Support Vector Machine method with radial basis kernel present the best results for the time cuto s considered (72 [hr] and 96 [hr]) over all the techniques studied with respect to prediction rates and number of the training sets. en
dc.format.extent p. 1-7 es
dc.language en es
dc.title Prediction of abnormal wine fermentations using computational intelligent techniques en
dc.type Articulo es
sedici.identifier.uri http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr15-1.pdf es
sedici.identifier.issn 1666-6038 es
sedici.creator.person Hernández, Gonzalo es
sedici.creator.person León, Roberto es
sedici.creator.person Urtubia, Alejandra es
sedici.subject.materias Ciencias Informáticas es
sedici.subject.decs Fermentación es
sedici.subject.decs Vino es
sedici.subject.other support vector machines en
sedici.description.fulltext true es
mods.originInfo.place Facultad de Informática es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc/3.0/
sedici.description.peerReview peer-review es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 15, no. 1 es
sedici.subject.acmcss98 Neural nets es

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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)