Learning in certainty-factor-based neural networks

Author

LiMin Fin

Author_Institution

Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL

Volume

1

fYear

1998

fDate

4-8 May 1998

Firstpage

45

Abstract

The certainty-factor-based neural network refers to a multilayer neural network where the network activation function is based on the certainty factor (CF) model of MYCIN-like systems. It is shown that the neural network using the CF-based activation function requires relatively small sample sizes for correct generalization and hence also facilitates learning rules. These findings are confirmed by empirical studies. Experiments suggest that the CFNet is capable of discovering the underlying domain rules

Keywords

case-based reasoning; generalisation (artificial intelligence); learning (artificial intelligence); multilayer perceptrons; transfer functions; MYCIN-like systems; certainty-factor-based neural networks; domain rules; generalization; learning rules; multilayer neural network; network activation function; Algorithm design and analysis; Artificial intelligence; Computer networks; Intelligent networks; Learning systems; Multi-layer neural network; Neural networks; Neurons; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.682234

Filename

682234