Quantizability and learning complexity in multilayer neural networks

被引：1

作者：

Fu, LM ^{[1
]}

机构：

[1] Univ Florida, Dept Comp & Informat Sci, Gainesville, FL 32611 USA

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS | 1998年 / 28卷 / 02期

基金：

美国国家科学基金会;

关键词：

generalization; learning complexity; machine learning; neural network; quantizability; sample complexity;

D O I：

10.1109/5326.669575

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The relationship between quantizability and learning complexity in multilayer neural networks is examined. In a special neural network architecture that calculates node activations according to the certainty factor (CF) model of expert systems, the analysis based upon quantizability leads to lower and also better estimates for generalization dimensionality and sample complexity than those suggested by the multilayer perceptron model. This analysis is further supported by empirical simulation results.

引用

页码：295 / 300

页数：6

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