SOME EXTENSIONS OF RADIAL BASIS FUNCTIONS AND THEIR APPLICATIONS IN ARTIFICIAL-INTELLIGENCE

Radial Basis Functions have recently found interesting applications in Artificial Intelligence, and in particular in the problem of learning to perform a particular task from a set of examples. However, in many practical cases the Radial Basis Functions method cannot be applied in a straight-forward manner, because it does not take into account some features that are typical of the problem of learning from examples. In this paper, we show some extensions of the standard theory, introduced in order to deal with a large number of examples and with problems in which different variables play very different roles. We present some examples and also point out some open problems.