Learning from Data Concepts, Theory and Methods 2e

Vladimir Cherkassky/ Filip M. Mulier

ISBN 10: 0471681822 / ISBN 13: 9780471681823

Editore: IEEE, 2007

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2nd edition. 538 pages. 9.75x6.50x1.25 inches. In Stock. Codice articolo x-0471681822

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Titolo: Learning from Data Concepts, Theory and ...

Casa editrice: IEEE

Data di pubblicazione: 2007

Legatura: Hardcover

Condizione: Brand New

Edizione: seconda edizione

Descrizione articolo

Riassunto:

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.

Recensione:

"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)

"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)