Knowledge Processing with Interval and Soft Computing

. Ed(s): Hu, Chenyi; Baker Kearfott, R.; Korvin, Andre De; Kreinovich, Vladik

ISBN 10: 1848003250 / ISBN 13: 9781848003255

Published by Springer London Ltd, 2008

About this Item

Interval computing combined with fuzzy logic is an emerging tool in studying artificial intelligence and knowledge processing (AIKP) applications. This accessible book, a must-read for those in AI, provides introductions for both interval and fuzzy computing. Editor(s): Hu, Chenyi; Baker Kearfott, R.; Korvin, Andre De; Kreinovich, Vladik. Series: Advanced Information and Knowledge Processing. Num Pages: 245 pages, 61 black & white illustrations, 22 black & white tables, biography. BIC Classification: UNF. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 15. Weight in Grams: 526. . 2008. Hardback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9781848003255

Bibliographic Details

• Title: Knowledge Processing with Interval and Soft Computing
• Publisher: Springer London Ltd
• Publication Date: 2008
• Language: English
• ISBN 10: 1848003250
• ISBN 13: 9781848003255
• Binding: Hardcover
• Condition: New

About this title

Synopsis

Interval computing combined with fuzzy logic has become an emerging tool in studying artificial intelligence and knowledge processing (AIKP) applications since it models uncertainties frequently raised in the field. This book provides introductions for both interval and fuzzy computing in a very accessible style.

Application algorithms covered in this book include quantitative and qualitative data mining with interval valued datasets, decision making systems with interval valued parameters, interval valued Nash games and interval weighted graphs. Successful applications in studying finance and economics, etc are also included.

This book can serve as a handbook or a text for readers interested in applying interval and soft computing for AIKP.

From the Back Cover

Massive datasets, made available today by modern technologies, present a significant challenge to scientists who need to effectively and efficiently extract relevant knowledge and information.

Due to their ability to model uncertainty, interval and soft computing techniques have been found to be effective in this extraction. This book provides coverage of the basic theoretical foundations for applying these techniques to artificial intelligence and knowledge processing.

The first three chapters provide the background needed for those who are unfamiliar with interval and soft computing techniques. The following chapters describe innovative algorithms and their applications to knowledge processing.

In particular, these chapters cover computing techniques for interval linear systems of equations, interval matrix singular-value decomposition, interval function approximation, and decision making with statistical and graph-based data processing. To enable these applications, the book presents a standards-based object-oriented interval computing environment in C++.

By providing the necessary background and summarizing recent results and successful applications, this self-contained book will serve as a useful resource for researchers and practitioners wanting to learn interval and soft computing techniques and apply them to artificial intelligence and knowledge processing.