Applicational aspects of support vector machines

The special emphasis of support vector machines (SVMs) on generalization ability makes this approach particularly interesting for real‐world applications with limited amounts of training data. In this paper we analyse the applicational aspects of SVMs, illustrating them with the step‐by‐step construction of a classifier for polymers by means of their mid‐infrared spectra. With this example we show how the main difficulties of a typical industrial classification task can be addressed using SVMs.