COMPARISON OF A NEURAL NETWORK AND A PIECEWISE LINEAR CLASSIFIER

被引：6

作者：

LO, ZP

BAVARIAN, B

机构：

[1] Department of Electrical Engineering, University of California at Irvine, Irvine

来源：

PATTERN RECOGNITION LETTERS | 1991年 / 12卷 / 11期

关键词：

NEURAL NETWORK; SELF-ORGANIZING FEATURE MAP; PIECEWISE LINEAR CLASSIFIER;

D O I：

10.1016/0167-8655(91)90001-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A neural network algorithm proposed by Kohonen is used to design a neural classifier. We use the algorithm to classify a real multiple-class data set obtained from ship images and compare the results with the results of the piecewise linear classifier. The error performance of the neural classifier is slightly better than the piecewise linear classifier, however the training algorithm is much simpler and easier to implement.

引用

页码：649 / 655

页数：7

共 12 条

[1] ART-2 - SELF-ORGANIZATION OF STABLE CATEGORY RECOGNITION CODES FOR ANALOG INPUT PATTERNS [J].