On using parametric string distances and vector quantization in designing syntactic pattern recognition systems

Abstract

Considers a fundamental problem in syntactic pattern recognition in which we are required to recognize a string from its noisy version. We assume that the system has a dictionary which is a collection of all the ideal representations of the objects in question. When a noisy sample has to be processed, the system compares it with every element in the dictionary based on a nearest-neighbor philosophy. This is typically achieved using three standard edit operations-substitution, insertion and deletion. To accomplish this, one usually assigns a distance for the elementary symbol operations, d(.,.), and the inter-pattern distance, D(.,.), is computed as a function of these symbol edit distances. In this paper, we consider the assignment of the inter-symbol distances in terms of the novel and interesting assignments-the parametric distances-introduced by Bunke et al. (1993). We show how the classifier can be trained to get the optimal parametric distance using vector quantization in the meta-space, and report classification results after such a training process. In all our experiments, the training was typically achieved in a very few iterations. The subsequent classification accuracy we obtained using this single-parameter scheme was 96.13%. The power of the scheme is obvious if we compare it to 96.67%, which is the accuracy of the scheme which uses the complete array of inter-symbol distances derived from a knowledge of all the confusion probabilities.