Foundations of Computational Intelligence | Volume 1: Learning and Approximation

Aboul-Ella Hassanien (u. a.)

ISBN 10: 364210164X / ISBN 13: 9783642101649

Published by Springer, 2010

About this Item

Foundations of Computational Intelligence | Volume 1: Learning and Approximation | Aboul-Ella Hassanien (u. a.) | Taschenbuch | xii | Englisch | 2010 | Springer | EAN 9783642101649 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 106954457

Bibliographic Details

• Title: Foundations of Computational Intelligence | Volume 1: Learning and Approximation
• Publisher: Springer
• Publication Date: 2010
• Language: English
• ISBN 10: 364210164X
• ISBN 13: 9783642101649
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 23D millimeters 235 millimeters width by 155 millimeters height by 23 millimeters depth
• Weight: 622 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Recent years have seen numerous applications across a variety of fields using various techniques of Computational Intelligence. This book, one of a series on the foundations of Computational Intelligence, is focused on learning and approximation.

From the Back Cover

Learning methods and approximation algorithms are fundamental tools that deal with computationally hard problems and problems in which the input is gradually disclosed over time. Both kinds of problems have a large number of applications arising from a variety of fields, such as algorithmic game theory, approximation classes, coloring and partitioning, competitive analysis, computational finance, cuts and connectivity, geometric problems, inapproximability results, mechanism design, network design, packing and covering, paradigms for design and analysis of approximation and online algorithms, randomization techniques, real-world applications, scheduling problems and so on. The past years have witnessed a large number of interesting applications using various techniques of Computational Intelligence such as rough sets, connectionist learning; fuzzy logic; evolutionary computing; artificial immune systems; swarm intelligence; reinforcement learning, intelligent multimedia processing etc.. In spite of numerous successful applications of Computational Intelligence in business and industry, it is sometimes difficult to explain the performance of these techniques and algorithms from a theoretical perspective. Therefore, we encouraged authors to present original ideas dealing with the incorporation of different mechanisms of Computational Intelligent dealing with Learning and Approximation algorithms and underlying processes.

This edited volume comprises 15 chapters, including an overview chapter, which provides an up-to-date and state-of-the art research on the application of Computational Intelligence for learning and approximation.