Learning from Data: Concepts, Theory, and Methods - Rilegato

Cherkassky, Vladimir; Mulier, Filip M.

Rilegato

ISBN 10: 0471681822 ISBN 13: 9780471681823

Casa editrice: IEEE, 2007

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.

Su questo libro

Informazioni sull?autore

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.

Dalla quarta di copertina

An interdisciplinary framework for learning methodologies?now revised and updated

Learning 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.

Dettagli dell'edizione

• Editore: IEEE
• Data di pubblicazione: 2007
• Lingua: Inglese
• ISBN 10: 0471681822
• ISBN 13: 9780471681823
• Rilegatura: Copertina rigida
• Numero edizione: 2
• Numero di pagine: 538
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile