Knowledge-Based Neurocomputing

Description

Knowledge-Based Neurocomputing

Publisher: The MIT Press
Number Of Pages: 500
Publication Date: 2000-02-25
Sales Rank: 1067941
ISBN / ASIN: 0262032740
EAN: 9780262032742
Binding: Hardcover
Manufacturer: The MIT Press
Studio: The MIT Press
Average Rating: 5

Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based neurocomputing, the emphasis is on the use and representation of knowledge about an application. Explicit modeling of the knowledge represented by such a system remains a major research topic. The reason is that humans find it difficult to interpret the numeric representation of a neural network.

The key assumption of knowledge-based neurocomputing is that knowledge is obtainable from, or can be represented by, a neurocomputing system in a form that humans can understand. That is, the knowledge embedded in the neurocomputing system can also be represented in a symbolic or well-structured form, such as Boolean functions, automata, rules, or other familiar ways. The focus of knowledge-based computing is on methods to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system.

Contributors: C. Aldrich, J. Cervenka, I. Cloete, R. A. Cozzio, R. Drossu, J. Fletcher, C. L. Giles, F. S. Gouws, M. Hilario, M. Ishikawa, A. Lozowski, Z. Obradovic, C. W. Omlin, M. Riedmiller, P. Romero, G. P. J. Schmitz, J. Sima, A. Sperduti, M. Spott, J. Weisbrod, J. M. Zurada.

Review:

Excellent for research!

Recommendation:
For graduate students
Basic:
This book is a set of papers about problem solving with Artificial Nueral Networks. So, you have to be well fimiliarized with the Neural Network concept. All these papers shows an approach to solve specific problems when creting an infernce machine related with a data base(Knowledge Base).
Intermediate:
Some knowledge on labeled graphs, and calculus can't be avoid. With this book you can : explore the several knowledge representation, introduce them into an Artificial Neural Network with testing and learning periods, and get the new rules generated once learning period ends.
Advance:
If you are developing an Expert System or a Knowledge-Based system you can't avoid use the Human Expert when you are designig the Knowledge Base. But, the expert would be only needed as a example provider for training and not as rule provider by using these book. So, the Artificial Neural Network works as the rule generator and inference engine.

http://mihd.net/lhv6i1

http://rapidshare.com/files/47493543/Cloete.rar.html

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