Knowledge Processing with Interval and Soft Computing (Advanced Information and Knowledge Processing)

Hu, Chenyi [Editor]; Baker Kearfott, R. [Editor]; de Korvin, Andre [Editor]; Kreinovich, Vladik [Editor];

ISBN 10: 1848003250 / ISBN 13: 9781848003255

Published by Springer, 2008

About this Item

Springer Verlag; London, 2008. Hardcover. A Near Fine, binding firm, interior and extremities tidy, minimal handling marks, book appears unread, mild board corner wear, without Dust wrapper. A nice, clean and unmarked copy. 8vo[octavo or approx. 6 x 9 inches], 233pp., indexed. We pack securely and ship daily with delivery confirmation on every book. The picture on the listing page is of the actual book for sale. Additional Scan(s) are available for any item, please inquire.Please note: Oversized books/sets MAY require additional postage then what is quoted for 2.2lb book.

Seller Inventory # SKU1037558

Bibliographic Details

• Title: Knowledge Processing with Interval and Soft Computing (Advanced Information and Knowledge Processing)
• Publisher: Springer
• Publication Date: 2008
• Language: English
• ISBN 10: 1848003250
• ISBN 13: 9781848003255
• Binding: hardcover
• Condition: As New
• Seller catalogs:
  • Math Text Books

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.