Designing syntactic pattern classifiers using vector quantization and parametric string editing
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.
|Journal||IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics|
Oommen, J, & Loke, R.K.S. (R. K.S.). (1999). Designing syntactic pattern classifiers using vector quantization and parametric string editing. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 29(6), 881–888. doi:10.1109/3477.809040