Feature Extraction: Foundations and Applications

Guyon, Isabelle (Editor)/ Gunn, Steve (Editor)/ Nikravesh, Masoud (Editor)/ Zadeh, Lofti A. (Editor)

ISBN 10: 366251771X / ISBN 13: 9783662517710

Editore: Springer-Verlag New York Inc, 2016

Riguardo questo articolo

reprint edition. 804 pages. 9.25x6.10x1.60 inches. In Stock.

Codice articolo x-366251771X

Dati bibliografici

• Titolo: Feature Extraction: Foundations and Applications
• Editore: Springer-Verlag New York Inc
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 366251771X
• ISBN 13: 9783662517710
• Rilegatura: Paperback
• Condizione: Brand New

Descrizione articolo

Riassunto

This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.

Dalla quarta di copertina

This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction.
"This book compiles some very promising techniques, coming from an extremely smart collection of researchers, delivering their best ideas in a competitive environment."
Trevor Hastie, Stanford University
"Feature selection is a key technology for making sense of the high dimensional data. Isabelle Guyon et al. have done a splendid job in designing a challenging competition, and collecting the lessons learned."
Bernhard Schoelkopf, Max Planck Institute
"There has been until now insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons. This volume is noteworthy for the breadth of methods covered, the clarity of presentations, the unity in notation and the helpful statistical appendices."
David G. Stork, Ricoh Innovations
"Feature extraction finds application in biotechnology, industrial inspection, the Internet, radar, sonar, and speech recognition. This book will make a difference to the literature on machine learning."
Simon Haykin, Mc Master University
"This book sets a high standard as the public record of an interesting and effective competition."
Peter Norvig, Google Inc.