Designing syntactic pattern classifiers using vector quantization and parametric string editing

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 29; no. 6; pp. 881 - 888
• Main Authors: Oommen, B.J., Loke, R.K.S.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.1999
• Subjects: Arrays; Classifiers; Computer science; Costs; Councils; Cybernetics; Dictionaries; Inference; Insertion; Pattern recognition; Phase noise; Philosophy; Probability distribution; System testing; Vector quantization
• DOI: 10.1109/3477.809040
• ISSN: 1083-4419

We consider a fundamental inference problem in syntactic pattern recognition (PR). We assume that the system has a dictionary which is a collection of all the ideal representations of the objects in question. To recognize a noisy sample, the system compares it with every element in the dictionary based on a nearest-neighbor philosophy, using three standard edit operations: substitution, insertion, and deletion, and the associated primitive elementary edit distances d(.,.). In this paper, we consider the assignment of the inter-symbol distances using the parametric distances. We show how the classifier can be trained to get the optimal parametric distance using vector quantization in the meta-space. 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 evident 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.