Knowledge acquisition: neural network learning

Guoyin Wang; Paul S. Fisher

doi:10.1117/12.381724

6 April 2000 Knowledge acquisition: neural network learning

Guoyin Wang, Paul S. Fisher

Proceedings Volume 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II; (2000) https://doi.org/10.1117/12.381724
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

As the amount of information in the world is steadily increasing, there is a growing demand for tools for analyzing the information. Many scholars have been working hard to study machine learning in order to obtain knowledge from domain data sets. They hope to find patterns in terms of implicit dependencies in data. Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Some scholars have done much work to interpret neural networks so that they will no longer be seen as black boxes and provided some plots and methods for knowledge acquisition using neural networks. These can be classified into three categories: fuzzy neural networks, CF (certainty factor) based neural networks, and logical neurons. We review some of these research works in this paper.

Citation Download Citation

Guoyin Wang and Paul S. Fisher "Knowledge acquisition: neural network learning", Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000); https://doi.org/10.1117/12.381724

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available