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 ba...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics man and cybernetics. Part B, Cybernetics, 1999-12, Vol.29 (6), p.881-888
Main Authors: Oommen, B.J., Loke, R.K.S.
Format: Article
Language:English
Subjects:
Arrays
Classifiers
Computer science
Costs
Councils
Cybernetics
Dictionaries
Inference
Insertion
Pattern recognition
Phase noise
Philosophy
Probability distribution
System testing
Vector quantization
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Summary: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.
ISSN:1083-4419
1941-0492
DOI:10.1109/3477.809040