Support Vector Regression in Spectrophotometry: An Experimental Study

In this work, we present a detailed experimental assessment of an interesting regression approach based on support vector machines (SVMs), a technique relatively recently introduced in the literature. The experimental framework reports a thorough investigation of the performance of SVMs from different viewpoints, including: (i) the influence of the kernel type in the SVM regression task; (ii) the sensitivity to the number of input variables (spectra dimension); (iii) the sensitivity to the available number of training samples; and (iv) the overall stability. The obtained results are compared with those yielded by the radial basis function (RBF) and the multilayer perceptron (MLP) neural networks as well as the traditional multiple linear regression (MLR) method on two different spectrophotometric datasets. © 2012 Copyright Taylor and Francis Group, LLC.