Pattern Recognition And Machine Learning - Couverture rigide

Bishop, Christopher M.

Couverture rigide

ISBN 10 : 0387310738 ISBN 13 : 9780387310732

Editeur : Springer-Verlag New York Inc., 2011

Synopsis

Hardcover

A propos du livre

Revue de presse :

"Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. ... Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. ... I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." --Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008

"In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of pattern recognition or machine learning . ... This book will serve as an excellent reference. ... With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop s book is a useful introduction ... and a valuable reference for the principle techniques used in these fields." --Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007

"This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." --John Maindonald for the Journal of Statistical Software

À propos de cette édition

• Éditeur: Springer-Verlag New York Inc.
• Date d'édition: 2011
• ISBN 10: 0387310738
• ISBN 13: 9780387310732
• Reliure: Relié
• Numéro d'édition: 1
• Nombre de pages: 738