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Optimizing the tuning parameters of least squares support vector machines regression for NIR spectra
Author(s) -
Coen T.,
Saeys W.,
Ramon H.,
De Baerdemaeker J.
Publication year - 2006
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.989
Subject(s) - overfitting , chemometrics , support vector machine , least squares support vector machine , partial least squares regression , least squares function approximation , nonlinear system , computer science , artificial neural network , kernel (algebra) , radial basis function , field (mathematics) , non linear least squares , artificial intelligence , pattern recognition (psychology) , machine learning , mathematics , statistics , explained sum of squares , physics , quantum mechanics , combinatorics , estimator , pure mathematics
Partial least squares (PLS) is one of the most used tools in chemometrics. Other data analysis techniques such as artificial neural networks and least squares support vector machines (LS‐SVMs) have however made their entry in the field of chemometrics. These techniques can also model nonlinear relations, but the presence of tuning parameters is a serious drawback. These parameters balance the risk of overfitting with the possibility to model the underlying nonlinear relation. In this work a methodology is proposed to initialize and optimize those tuning parameters for LS‐SVMs with radial basis function (RBF)‐kernel based on a statistical interpretation. In this way, these methods become much more appealing for new users. The presented methods are applied on manure spectra. Although this dataset is only slightly nonlinear, good results were obtained. Copyright © 2007 John Wiley & Sons, Ltd.