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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Published in | IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 29; no. 6; pp. 881 - 888 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
United States
IEEE
01.12.1999
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Subjects | |
Online Access | Get full text |
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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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 |
ISSN: | 1083-4419 1941-0492 |
DOI: | 10.1109/3477.809040 |