We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision

This paper presents a novel approach to the analysis of the overtraining phenomenon in pattern classifiers. A nonlinearity measure N is introduced which relates the shape of the classification function to the generalization capability of a classifier. Experiments using the k-nearest neighbour rule

. This is because they have the dual control problem of identifying their environment by classification whilst controlling it, and performance feedback is both relative and global. This paper considers five problem areas in the utilization of pattern classifying control systems: the derivation of performance

-scale problems and genetic algorithms are suitable for large-scale problems. � 1999 Pattern Recognition Society. Published

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters

This paper is a survey of research on pattern classifier. In particular, it emphasizes on the different types of pattern classifiers and their performance factors. Pattern classifiers use the algorithms of pattern recognition to classify various input classes into their respective categories

, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved

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An investigation is carried out to formulate some theoretical results regarding the behavior of a genetic-algorithm-based pattern classification methodology, for an infinitely large number of training data points n, inanN-dimensional space R�. It is proved that for nPR, and for a sufficiently large

Abstract not found