Learning from Data: Concepts, Theory, and Methods

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Vladimir Cherkassky (University of Minnesota, USA) Filip M. Mulier (University of Minnesota, USA)

Learning from Data

Concepts, Theory, and Methods

Vladimir Cherkassky (University of Minnesota, USA), Filip M. Mulier (University of Minnesota, USA)

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English

Wiley-IEEE Press
09 August 2007

Machine learning

Series: IEEE Press

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.

By: Vladimir Cherkassky (University of Minnesota USA), Filip M. Mulier (University of Minnesota, USA)
Imprint: Wiley-IEEE Press
Country of Publication: United States
Edition: 2nd edition
Dimensions: Height: 243mm, Width: 165mm, Spine: 34mm
Weight: 916g
ISBN: 9780471681823
ISBN 10: 0471681822
Series: IEEE Press
Pages: 560
Publication Date: 09 August 2007
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

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.

Reviews for Learning from Data: Concepts, Theory, and Methods

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)