DISPERSION EFFECT ON GENERALIZATION ERROR IN CLASSIFICATION - Experimental Proof and Practical Algorithm
Recent theoretical work proposes criteria of dispersion to generate learning points. The aim of this paper is to convince the reader, with experimental proofs, that dispersion is a good criterion in practice for generating learning points for classification problems. Problem of generating learning points consists then in generating points with the lowest dispersion. As a consequence, we present low dispersion algorithms existing in the literature, analyze them and propose a new algorithm.