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Designing syntactic pattern classifiers using vector quantization and parametric string editing

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2 Author(s)
Oommen, B.J. ; Sch. of Comput. Sci., Carleton Univ., Ottawa, Ont., Canada ; Loke, R.K.S.

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

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:29 ,  Issue: 6 )