Learning from Data: Concepts, Theory, and Methods, 2nd Edition

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Book Description Publication Date: August 24, 2007 | ISBN-10: 0471681822 | ISBN-13: 978-0471681823 | Edition: 2 An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy log...
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Book Description

Publication Date: August 24, 2007 | ISBN-10: 0471681822 | ISBN-13: 978-0471681823 | Edition: 2
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 making this an invaluable text.

Editorial Reviews

Review

"I think Learning From Data is a very valuable volume. I will recommend it to my graduate students." (Journal of the American Statistical Association, March 2009)

"The broad spectrum of information it offers is beneficial to many field of research. The selection of topics is good, and I believe that many researchers and practioners will find this book useful." (Technometrics, May 2008)

"The authors have succeeded in summarizing some of the recent trends and future challenges in different learning methods, including enabling technologies and some interesting practical applications." (Computing Reviews, May 22, 2008)

 

From the Back Cover

An interdisciplinary framework for learning methodologies—now revised and updated

Learning from Data 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 pattern recognition can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science.

Since the first edition was published, the field of data-driven learning has experienced rapid growth. This Second Edition covers these developments with a completely revised chapter on support vector machines, a new chapter on noninductive inference and alternative learning formulations, and an in-depth discussion of the VC theoretical approach as it relates to other paradigms.

Complete with over one hundred illustrations, case studies, examples, and chapter summaries, Learning from Data 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.

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