Articles liés à Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning - Couverture rigide

Bishop, Christopher M.

 
9780387310732: Pattern Recognition and Machine Learning
Couverture rigide
ISBN 10 : 0387310738 ISBN 13 : 9780387310732
Editeur : Springer-Verlag New York Inc., 2006

Synopsis

This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical models to describe probability distributions when there are no other books that apply graphical models to machine learning. It is also the first four-color book on pattern recognition. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher.

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À propos de l'auteur

​Chris Bishop is a Microsoft Distinguished Scientist and the Laboratory Director at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, and in 2007 he was elected Fellow of the Royal Society of Edinburgh.
Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory. He then joined Culham Laboratory where he worked on the theory of magnetically confined plasmas as part of the European controlled fusion programme.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Springer-Verlag New York Inc.
Date d'édition
2006
Langue
anglais
ISBN 10 
0387310738
ISBN 13 
9780387310732
Reliure
Relié
Numéro d'édition
1
Nombre de pages
798
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