Pattern Recognition and Machine Learning

Christopher M Bishop

ISBN 10: 0387310738 / ISBN 13: 9780387310732

Edité par Springer New York Aug 2006, 2006

A propos de cet article

Neuware -Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reacts these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. 778 pp. Englisch.

N° de réf. du vendeur 9780387310732

Détails bibliographiques

• Titre: Pattern Recognition and Machine Learning
• Éditeur: Springer New York Aug 2006
• Date de parution: 2006
• Langue: anglais
• ISBN à 10 chiffres: 0387310738
• ISBN à 13 chiffres: 9780387310732
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 260 L x 185 H x 40 P (en millimètres) 260 millimètres de largeur par 185 millimètres de hauteur par 40 millimètres de profondeur
• Poids: 1482 grammes

A propos de ce titre

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.

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.