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Designing syntactic pattern classifiers using vector quantization and parametric string editing.

B J Oommen1, R S Loke

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This study introduces an optimal parametric distance for syntactic pattern recognition (PR). The new method achieves high classification accuracy (96.13%) with efficient training.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Syntactic pattern recognition (PR) relies on comparing noisy samples to ideal dictionary representations.
  • Standard PR methods use edit distances (substitution, insertion, deletion) for comparison.

Purpose of the Study:

  • To develop an optimal parametric distance for inter-symbol distances in PR.
  • To train a classifier using vector quantization for optimal parameter assignment.

Main Methods:

  • Utilized parametric distances for inter-symbol distance assignment.
  • Employed vector quantization in meta-space for classifier training.
  • Evaluated performance using nearest-neighbor philosophy with edit operations.

Main Results:

  • Achieved a classification accuracy of 96.13% with the single-parameter scheme.
  • Demonstrated efficient training, typically completed in very few iterations.
  • Showcased the effectiveness of the parametric distance compared to traditional methods.

Conclusions:

  • The proposed parametric distance assignment is effective for syntactic pattern recognition.
  • Vector quantization provides an efficient method for training optimal PR classifiers.
  • The single-parameter scheme offers a powerful alternative to complex distance calculations.