A cloning approach to classifier training

被引：8

作者：

Al-Alaoui, MA ^{[1
]}

Mouci, R

Mansour, MM

Ferzli, R

机构：

[1] Amer Univ Beirut, Dept Elect & Comp Engn, Beirut, Lebanon

[2] Cent Bank Lebanon, Beirut, Lebanon

[3] Univ Illinois, Coordinated Sci Lab, Elect & Comp Engn Dept, Urbana, IL 61801 USA

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS | 2002年 / 32卷 / 06期

关键词：

Al-Alaoui algorithm; back-propagation algorithm; Bayes classifier; character recognition; Levenberg-Marquardt algorithm; neural networks; pattern classification;

D O I：

10.1109/TSMCA.2002.807035

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The Al-Alaoui algorithm is a weighted mean-square error (MSE) approach to pattern recognition. It employs cloning of the erroneously classified samples to increase the population of their corresponding classes. The algorithm was originally developed for linear classifiers. In this paper, the algorithm is extended to multilayer neural networks which may be used as nonlinear classifiers. It is also shown that the application of the Al-Alaoui algorithm to multilayer neural networks speeds up the convergence of the back-propagation algorithm.

引用

页码：746 / 752

页数：7

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