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This paper discusses the two class classification problem using discriminant function solution that minimizes the probability of classification error. Learning algorithms using window function techniques are presented. The convergence rates are estimated and a particular strategy is proposed. Within this strategy it is recommended to use a triangular window function. The proposed algorithms are tested on several artificial pattern classification problems and their efficiency is proven. A comparison with the mean-square-error algorithm is also presented.
window functions, Discriminant functions, machine learning, pattern recognition, stochastic approximation

M. Installe and n. Hai Do-Tu, "Learning Algorithms for Nonparametric Solution to the Minimum Error Classification Problem," in IEEE Transactions on Computers, vol. 27, no. , pp. 648-659, 1978.
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