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Learning from Data: Concepts, Theory, and Methods

Learning from Data: Concepts, Theory, and Methods

by VladimirCherkassky (Author), Filip M . Mulier (Author)

  • New

    Hardcover 2007

    $162.91
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 making this an invaluable text.

$162.91

Quantity

20+ in stock

More Information

Format: Hardcover
 Pages: 538
 Edition: 2nd Revised edition
 Publisher: Wiley-Blackwell
Published: 11 Sep 2007
ISBN 10: 0471681822
ISBN 13: 9780471681823

Media Reviews
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)
Author Bio
Vladimir CherKassky, PhD, is Professor of Electrical and Computer Engineering at the University of Minnesota. He is internationally known for his research on neural networks and statistical learning. Filip Mulier, PhD, has worked in the software field for the last twelve years, part of which has been spent researching, developing, and applying advanced statistical and machine learning methods. He currently holds a project management position.