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Proceedings Paper

Learning the optimal discriminant function through genetic learning algorithm
Author(s): James Zhen Tu; Ernest L. Hall
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Paper Abstract

The problem of learning correct decision rules to minimize the probability of misclassification is a problem of supervised learning in pattern recognition. The problem of learning such optimal discriminant function is considered for the class of problems where little is known about the statistical properties of the pattern classes. This paper describes the application of a machine learning technique called the genetic learning algorithm to the problem of learning the optimal discriminant function. Several variations of the algorithm are investigated to determine which generates the best solution. Simulation results and examples are presented. The main advantages offered by the genetic algorithm are generality and fast learning.

Paper Details

Date Published: 1 February 1992
PDF: 12 pages
Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57097
Show Author Affiliations
James Zhen Tu, Univ. of Cincinnati (United States)
Ernest L. Hall, Univ. of Cincinnati (United States)


Published in SPIE Proceedings Vol. 1607:
Intelligent Robots and Computer Vision X: Algorithms and Techniques
David P. Casasent, Editor(s)

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