Learning from Data : Concepts, Theory, and Methods

Vladimir Cherkassky

ISBN 10: 0471681822 / ISBN 13: 9780471681823

Published by Wiley Aug 2007, 2007

About this Item

Neuware - An interdisciplinary framework for learning methodologies-now revised and updatedLearning from Data 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 pattern recognition can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science.Since the first edition was published, the field of data-driven learning has experienced rapid growth. This Second Edition covers these developments with a completely revised chapter on support vector machines, a new chapter on noninductive inference and alternative learning formulations, and an in-depth discussion of the VC theoretical approach as it relates to other paradigms.Complete with over one hundred illustrations, case studies, examples, and chapter summaries, Learning from Data 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.

Seller Inventory # 9780471681823

Bibliographic Details

• Title: Learning from Data : Concepts, Theory, and Methods
• Publisher: Wiley Aug 2007
• Publication Date: 2007
• Language: English
• ISBN 10: 0471681822
• ISBN 13: 9780471681823
• Binding: Buch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 240W x 161H x 34D millimeters 240 millimeters width by 161 millimeters height by 34 millimeters depth
• Weight: 997 grams

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.