Learning from Data Concepts, Theory and Methods 2e

Vladimir Cherkassky/ Filip M. Mulier

ISBN 10: 0471681822 / ISBN 13: 9780471681823

Published by IEEE, 2007

About this Item

2nd edition. 538 pages. 9.75x6.50x1.25 inches. In Stock. This item is printed on demand.

Seller Inventory # __0471681822

Bibliographic Details

• Title: Learning from Data Concepts, Theory and Methods 2e
• Publisher: IEEE
• Publication Date: 2007
• Language: English
• ISBN 10: 0471681822
• ISBN 13: 9780471681823
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: Brand New

About this title

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.

About the Author

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.