Learning in certainty-factor-based multilayer neural networks for classification

被引：17
|
作者：
Fu, LM [1 ]
机构：
[1] Univ Florida, Dept Comp & Informat Sci, Gainesville, FL 32611 USA
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 1998年 / 9卷 / 01期
基金：
美国国家科学基金会;
关键词：
certainty factor; expert network; generalization; machine learning; neural network; sample complexity;
D O I：
10.1109/72.655036
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The computational framework of rule-based neural networks inherits from the neural network and the inference engine of an expert system. In one approach, the network activation function is based on the certainty factor (CF) model of MYCIN-like systems, In this paper, it is shown theoretically that the neural network using the CF-based activation function requires relatively small sample sizes for correct generalization. This result is also confirmed by empirical studies in,several independent domains.
引用
收藏
页码：151 / 158
页数：8
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