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Pattern Recognition & Machine Learning by Christopher M Bishop-- Hardcover

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Specificaties

Objectstaat
Heel goed: Een boek dat er niet als nieuw uitziet en is gelezen, maar zich in uitstekende staat ...
Pages
778
Publication Date
2006-08-17
Book Title
Pattern Recognition and Machine Learning
ISBN
9780387310732
Publication Name
Pattern Recognition and Machine Learning
Item Length
9.3in
Publisher
Springer New York
Series
Information Science and Statistics Ser.
Publication Year
2011
Type
Textbook
Format
Hardcover
Language
English
Item Height
0.7in
Author
Christopher M. Bishop
Item Width
7in
Item Weight
75.7 Oz
Number of Pages
Xx, 778 Pages

Over dit product

Product Information

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 ?eld, 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 signi'cant impact on both algorithms and applications. This new textbook re'ects these recent developments while providing a comp- hensive introduction to the ?elds of pattern recognition and machine learning. It is aimed at advanced undergraduates or ?rst year PhD students, as well as 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 - sential as the book includes a self-contained introduction to basic probability theory.

Product Identifiers

Publisher
Springer New York
ISBN-10
0387310738
ISBN-13
9780387310732
eBay Product ID (ePID)
51822478

Product Key Features

Author
Christopher M. Bishop
Publication Name
Pattern Recognition and Machine Learning
Format
Hardcover
Language
English
Series
Information Science and Statistics Ser.
Publication Year
2011
Type
Textbook
Number of Pages
Xx, 778 Pages

Dimensions

Item Length
9.3in
Item Height
0.7in
Item Width
7in
Item Weight
75.7 Oz

Additional Product Features

Number of Volumes
1 Vol.
Lc Classification Number
Q337.5
Reviews
"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, From the reviews: "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 "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)
Table of Content
Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
Copyright Date
2006
Topic
Probability & Statistics / General, Intelligence (Ai) & Semantics, Cognitive Psychology & Cognition, Computer Vision & Pattern Recognition
Lccn
2006-922522
Dewey Decimal
006.4
Intended Audience
Scholarly & Professional
Dewey Edition
22
Illustrated
Yes
Genre
Computers, Psychology, Mathematics

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