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Feature evaluation criteria and contextual decoding algorithms in statistical pattern recognition Toussaint, Godfried Theodore Patrick

Abstract

Several probabilistic distance, information, uncertainty, and overlap measures are proposed and considered as possible feature evaluation criteria for statistical pattern recognition problems. A theoretical and experimental analysis .and comparison of these and other well known criteria are presented. A class of certainty measures is proposed that is well suited to pre-evaluation -and intraset post-evaluation. Two approaches to the M-class problem are considered: the well known expected value approach and a proposed generalized distance approach. In both approaches the main distance measures considered are Kullback's divergence, the Bhattacharyya coefficient, and Kolmogorov's variational distance. In addition a generalization of the Bhattacharyya coefficient to the M-class problem is considered. Properties and inequalities are derived for the above measures. In addition, error bounds are derived whenever possible, and it is shown that some of these bounds are tighter than existing ones while others represent generalizations of well known bounds found in the literature. Feature ordering experiments were carried out to compare the above measures with each other and with the Bayes error probability criterion. Finally, some very general measures are proposed which reduce to several of the measures considered above as special cases and error bounds are derived in terms of these measures. A general class of algorithms is proposed for the efficient utilization of contextual dependencies among patterns, at the syntactic level, in the decision process of statistical pattern recognition systems. The algorithms are dependent on a set of parameters which determine the amount of contextual information to be used, how it is to be processed, and the computation and storage required. One of the most important parameters in question, referred to as the depth of search, determines the number of alternatives considered in the decoding process. Two general types of algorithms are proposed in which the depth of search is pre-specified and remains fixed. The efficiency of these algorithms is increased by incorporating a threshold and effecting a variable depth of search that depends on the amount of noise present in the pattern in question. Several general types of algorithms incorporating the threshold are also proposed. In order to experimentally analyze, compare, and evaluate the various algorithms, the recognition of handprinted English text was simulated on a digital computer.

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