Support vector regression for functional data in multivariate calibration problems

dc.creatorHernandez, N
dc.creatorTalavera, I
dc.creatorBiscay, RJ
dc.creatorPorro, D
dc.creatorFerreira, MMC
dc.date2009
dc.dateMAY 29
dc.date2014-11-14T03:14:23Z
dc.date2015-11-26T17:12:53Z
dc.date2014-11-14T03:14:23Z
dc.date2015-11-26T17:12:53Z
dc.date.accessioned2018-03-29T00:01:17Z
dc.date.available2018-03-29T00:01:17Z
dc.identifierAnalytica Chimica Acta. Elsevier Science Bv, v. 642, n. 41671, n. 110, n. 116, 2009.
dc.identifier0003-2670
dc.identifierWOS:000266414300015
dc.identifier10.1016/j.aca.2008.10.063
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/74823
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/74823
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/74823
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1281441
dc.descriptionQuantitative analyses involving instrumental signals, such as chromatograms, NIR, and MIR spectra have successfully applied nowadays for the solution of important chemical tasks. Multivariate calibration is very useful for such purposes and the commonly used methods in chemometrics consider each sample spectrum as a sequence of discrete data points. An alternative way to analyze spectral data is to consider each sample as a function, in which a functional data is obtained. Concerning regression, some linear and nonparametric regression methods have been generalized to functional data. This paper proposes the use of the recently introduced method, support vector regression for functional data (FDA-SVR) for the solution of linear and nonlinear multivariate calibration problems. Three different spectral datasets were analyzed and a comparative study was carried out to test its performance with respect to some traditional calibration methods used in chemometrics such as PLS, SVR and LS-SVR. The satisfactory results obtained with FDA-SVR suggest that it can be an effective and promising toot for multivariate calibration tasks. (C) 2008 Elsevier B.V. All rights reserved.
dc.description642
dc.description41671
dc.description110
dc.description116
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationAnalytica Chimica Acta
dc.relationAnal. Chim. Acta
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectSupport vector regression
dc.subjectFunctional Data Analysis
dc.subjectMultivariate calibration
dc.subjectChemometrics
dc.subjectVariables
dc.subjectSelection
dc.subjectMachines
dc.subjectSpectra
dc.subjectModels
dc.subjectTools
dc.titleSupport vector regression for functional data in multivariate calibration problems
dc.typeArtículos de revistas


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