On domain knowledge and feature selection using a support vector machine

The basic principles of a support vector machine (SVM) are analyzed. The problem of feature selection while using an SVM is specifically addressed. An approach to constructing a kernel function which takes into account some domain knowledge about a problem and thus essentially diminishes the number of noisy parameters in high dimensional feature space is suggested. Its application to Texture Recognition is described.