Adaptive and Learning Systems for Signal Processing, Communications and Control: Learning from Data: Concepts, Theory, and Methods

Cherkassky, Vladimir

ISBN 10: 0471154938 / ISBN 13: 9780471154938

Published by John Wiley and Sons, 1998

About this Item

This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Book contains pencil markings. In poor condition, suitable as a reading copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,850grams, ISBN:9780471154938.

Seller Inventory # 8676633

Bibliographic Details

• Title: Adaptive and Learning Systems for Signal Processing, Communications and Control: Learning from Data: Concepts, Theory, and Methods
• Publisher: John Wiley and Sons
• Publication Date: 1998
• Language: English
• ISBN 10: 0471154938
• ISBN 13: 9780471154938
• Binding: Hardcover
• Condition: Poor
• Seller catalogs:
  • Engineering

About this title

Synopsis

An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic This book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples, Learning from Data:
* Relates statistical formulation with the latest methodologies used in artificial neural networks, fuzzy systems, and wavelets
* Features consistent terminology, chapter summaries, and practical research tips
* Emphasizes the conceptual framework provided by Statistical Learning Theory (VC-theory) rather than its commonly practiced mathematical aspects
* Provides a detailed description of the new learning methodology called Support Vector Machines (SVM)
This invaluable text/reference accommodates both beginning and advanced graduate students in engineering, computer science, and statistics. It is also indispensable for researchers and practitioners in these areas who must understand the principles and methods for learning dependencies from data.

About the Author

VLADIMIR CHERKASSKY is on the faculty of electrical and computer engineering at the University of Minnesota. His current research is on neural network and statistical methods for estimating dependencies from data. Professor Cherkassky is on the governing board of the International Neural Network Society (INNS). He was an organizer of the NATO Advanced Study Institute symposium, From Statistics to Neural Networks, held in France in 1993. FILIP MULIER received a PhD in electrical engineering from the University of Minnesota in 1994. He currently works with a large multinational corporation on industrial applications of learning methods. His current research is on practical applications of learning theory, including industrial process control and financial market prediction.