Adaptive resolution Min-Max classifier

被引：0

作者：

Rizzi, A ^{[1
]}

Mascioli, FMF ^{[1
]}

Martinelli, G ^{[1
]}

机构：

[1] Univ Rome La Sapienza, INFO COM Dept, I-00184 Rome, Italy

来源：

1998 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AT THE IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE - PROCEEDINGS, VOL 1-2 | 1998年

关键词：

neuro-fuzzy networks; pattern recognition classification; constructive algorithms; learning theory; Min-Max networks;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the present paper a new neuro-fuzzy classifier, inspired by the Simpson's Min-Max model, is presented By relying on a constructive approach, it overcomes some undesired properties of the original Min-Max algorithm. In particular, training result does not depend on pattern presentation order and hyperbox expansion is not limited by a fixed maximum size, so that it is possible to have different covering resolutions. Consequently, the new algorithm yields less complex networks, increasing the generalization capability in accordance with Learning Theory paradigms. Several rests are presented for illustration.

引用

页码：1435 / 1440

页数：6

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