Learning the optimal discriminant function through genetic learning algorithm
James Z. Tu, Ernest L. Hall · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
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.