On global, local, mixed and neighborhood kernels for support vector machines

The basic principles of the support vector machine (SVM) are analyzed. Two approaches to constructing a kernel function which takes into account some local properties of a problem are considered. The first one deals with interactions between neighboring pixels in an image and the second with proximity of the objects in the input space. In the former case, this is equivalent to feature selection and the efficiency of this approach is demonstrated by an application to Texture Recognition. In the latter case, this approach may be considered as either a kind of local algorithm or as a mixture of local and global ones. We demonstrate that the use of such kernels increases the domain of SVM applications.