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Pattern Recognition and Machine Learning [Information Science and Statistics]
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Item specifics
- Condition
- ISBN
- 9780387310732
- Subject Area
- Mathematics, Computers, Psychology
- Publication Name
- Pattern Recognition and Machine Learning
- Publisher
- Springer New York
- Item Length
- 9.3 in
- Subject
- Probability & Statistics / General, Intelligence (Ai) & Semantics, Cognitive Psychology & Cognition, Computer Vision & Pattern Recognition
- Publication Year
- 2006
- Series
- Information Science and Statistics Ser.
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 0.7 in
- Item Weight
- 75.7 Oz
- Item Width
- 7 in
- Number of Pages
- Xx, 778 Pages
About this product
Product Identifiers
Publisher
Springer New York
ISBN-10
0387310738
ISBN-13
9780387310732
eBay Product ID (ePID)
51822478
Product Key Features
Number of Pages
Xx, 778 Pages
Publication Name
Pattern Recognition and Machine Learning
Language
English
Subject
Probability & Statistics / General, Intelligence (Ai) & Semantics, Cognitive Psychology & Cognition, Computer Vision & Pattern Recognition
Publication Year
2006
Type
Textbook
Subject Area
Mathematics, Computers, Psychology
Series
Information Science and Statistics Ser.
Format
Hardcover
Dimensions
Item Height
0.7 in
Item Weight
75.7 Oz
Item Length
9.3 in
Item Width
7 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2006-922522
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)
Dewey Edition
22
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
006.4
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.
Synopsis
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., The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, 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 techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects 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 well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. 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 byinstructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. Coming soon: *For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text) *For instructors, worked solutions to remaining exercises from the Springer web site *Lecture slides to accompany each chapter *Data sets available for download, 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., This is the first textbook on pattern recognition to present the Bayesian viewpoint. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible, and it uses graphical models to describe probability distributions., 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.
LC Classification Number
Q337.5
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- v***u (253)- Feedback left by buyer.Past yearVerified purchaseThere was a issue with this manual, the seller didn't realize it was a 2 volume set. Only received volume 2 of 2. When contacted, seller took responsibility for an inaccurate listing, and offered an agreeable solution to the dilemma. In the end, I am happy with the resolution. Mistakes happen, a good seller takes responsibility and does the right thing. Win/Win for both parties.Volkswagen Passat [B5] Service Manual: 1998, 1999, 2000, 2001, 2002, 2003, 2004, (#305872213162)
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Product ratings and reviews
Most relevant reviews
- Apr 08, 2021
Great printing quality, like brand new. Great value!
Verified purchase: YesCondition: Pre-OwnedSold by: erics_place
- Mar 19, 2019
Good quality.
Verified purchase: YesCondition: NewSold by: booksaffordablepenny
- Mar 19, 2025
Very nice book. High quality....
Verified purchase: YesCondition: Pre-OwnedSold by: the_publisher
- Jan 26, 2016
A good and comprehensive book about machine learning.
Verified purchase: YesCondition: NewSold by: mikes-bookstore