Learning Algorithms for Nonparametric Solution to the Minimum Error Classification Problem
- 1 July 1978
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Computers
- Vol. C-27 (7), 648-659
- https://doi.org/10.1109/tc.1978.1675165
Abstract
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.Keywords
This publication has 6 references indexed in Scilit:
- Discriminant function implementation of a minimum risk classifierBiological Cybernetics, 1975
- Training a One-Dimensional Classifier to Minimize the Probability of ErrorIEEE Transactions on Systems, Man, and Cybernetics, 1972
- Design of Pattern Classifiers with the Updating Property Using Stochastic Approximation TechniquesIEEE Transactions on Computers, 1968
- A Theory of Adaptive Pattern ClassifiersIEEE Transactions on Electronic Computers, 1967
- Generation of Polynomial Discriminant Functions for Pattern RecognitionIEEE Transactions on Electronic Computers, 1967
- Linear Decision Functions, with Application to Pattern RecognitionProceedings of the IRE, 1962