Cart (Loading....) | Create Account
Close category search window
 

A neural-network model for learning domain rules based on its activation function characteristics

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Limin Fu ; Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL, USA

A challenging problem in machine learning is to discover the domain rules from a limited number of instances. In a large complex domain, it is often the case that the rules learned by the computer are at most approximate. To address this problem, this paper describes the CFNet which bases its activation function on the certainty factor (CF) model of expert systems. A new analysis on the computational complexity of rule learning in general is provided. A further analysis shows how this complexity can be reduced to a point where the domain rules can be accurately learned by capitalizing on the activation function characteristics of the CFNet. The claimed capability is adequately supported by empirical evaluations and comparisons with related systems

Published in:

Neural Networks, IEEE Transactions on  (Volume:9 ,  Issue: 5 )

Date of Publication:

Sep 1998

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.