Learning from Data: Concepts, Theory, and Methods (Adaptive and Learning Systems for Signal Processing, Communications and Control Series) [Hardcover]

Vladimir Cherkassky (Author), Filip M. Mulier (Author)

Book Description

ISBN-10: 0471154938 | ISBN-13: 978-0471154938 | Publication Date: March 25, 1998 | Edition: 1

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.

Editorial Reviews

Review

"This book contains considerable information on the concept of statistical learning theory.... However, some may find its presentation difficult to follow..." (Technometrics, February, 2001)

"...well readable..." (Zentralblatt Math, Vol.960, No.10 2001)

From the Publisher

This is an interdisciplinary book on neural networks, statistics and fuzzy systems. A unique feature is the establishment of a general framework for adaptive data modeling within which various methods from statistics, neural networks and fuzzy logic are presented. Chapter summaries, examples and case studies are also included. Includes companion Web site with ... Software for use with the book.

See all Editorial Reviews

Product Details

• Hardcover: 464 pages
• Publisher: Wiley-Interscience; 1 edition (March 25, 1998)
• Language: English
• ISBN-10: 0471154938
• ISBN-13: 978-0471154938
• Product Dimensions: 9.2 x 6.2 x 1.2 inches
• Shipping Weight: 1.7 pounds
• Average Customer Review: 4.8 out of 5 stars  See all reviews (4 customer reviews)
• Amazon Best Sellers Rank: #1,672,578 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

11 of 11 people found the following review helpful:

4.0 out of 5 stars An up to date, unifying textbook on learning/modelling depen, December 19, 2001

By 

Oliver Femminella (London, United Kingdom) - See all my reviews

This review is from: Learning from Data: Concepts, Theory, and Methods (Adaptive and Learning Systems for Signal Processing, Communications and Control Series) (Hardcover)

The material contained in the textbook presents and discusses recent developments, but also important statistical (learning theory) concepts such as model selection, regularisation etc, in a unifying manner.
Although the authors are somewhat biased towards kernel methods, support vector machines in particular, they discuss the applicability and performance of other methods (neural networks, fuzzy systems, etc.). This is to be commended, as there are not many books that discuss all such methods in a common framework.
This book is highly recommended to readers wishing to gain a good understanding of the most significant statistical and other methods being applied in industry, and continuously experiencing significant academic research. A set of very good references (some mandatory and well known in the research community) presented at the end of each chapter directs the reader to some very useful material and scientific publications. This is a book that will particularly appeal to the research/academic community.

13 of 16 people found the following review helpful:

5.0 out of 5 stars Study in easy, August 19, 2000

By A Customer

This review is from: Learning from Data: Concepts, Theory, and Methods (Adaptive and Learning Systems for Signal Processing, Communications and Control Series) (Hardcover)

This book is excellent and easy to study. Graduate students will find the book statistical learning theory and support vector machines(SVMs),especially learning system based on recent advances in machine learning and multiobjective optimization. This book describes the Vapnik and Chervonenkis(VC) theory's generalization abilities. For statisticians, Applied mathematician, mechanical engineers and most graduate student are interested in reading this book. This is a very good excellent reference!!

3 of 3 people found the following review helpful:

5.0 out of 5 stars read into it, May 6, 2009

By 

Machine will be able to learn "ML" (USA) - See all my reviews

This review is from: Learning from Data: Concepts, Theory, and Methods (Kindle Edition)

This book introducing the general idea of learning from data, aka, machine leanring, data mining, etc, using a plain language. The algorithms and techniques described are very useful in pratice, although it may seems ad-hoc in the beginning. The whole field of statistical learning theory is very complicated (see the proceedings of COLT/ALT/etc). This book describes it in a straightforward and application-oriented way. Recommend to read. It is kind of pricey, though.