USING MULTITHRESHOLD QUADRATIC SIGMOIDAL NEURONS TO IMPROVE CLASSIFICATION CAPABILITY OF MULTILAYER PERCEPTRONS

被引：4

作者：

CHIANG, CC ^{[1
]}

FU, HC ^{[1
]}

机构：

[1] NATL CHIAO TUNG UNIV,DEPT COMP SCI & INFORMAT ENGN,HSINCHU 300,TAIWAN

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 1994年 / 5卷 / 03期

关键词：

D O I：

10.1109/72.286930

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This letter proposes a new type of neurons called multithreshold quadratic sigmoidal neurons to improve the classification capability of muitilayer neural networks. In cooperation with single-threshold quadratic sigmoidal neurons, the multithreshold quadratic sigmoidal neurons can be used to improve the classification capability of multilayer neural networks by a factor of four compared to committee machines and by a factor of two compared to the conventional sigmoidal multilayer perceptrons.

引用

页码：516 / 519

页数：4

共 13 条

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