SELECTION OF INFORMATIVE FEATURES USING HEURISTIC CRITERIA OF FISHER TYPE

At present the most popular criteria are of informative heuristic criteria associated with the estimation of separability given classes and based on the fundamental pattern recognition compactness hypothesis: with increasing distance between the classes improved their separability. "Good" are those features that maximize the distance. Such heuristic criteria, although are widely used in solving practical problems of classification, but in theory are scarcely explored.