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Mustererkennung und maschinelles Lernen Christopher M. Bishop Buch Lehrbuch-
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eBay-Artikelnr.:365834251631
Artikelmerkmale
- Artikelzustand
- Release Year
- 2006
- Book Title
- Pattern Recognition and Machine Learning (Information Science ...
- ISBN
- 9780387310732
Über dieses Produkt
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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Produktbewertungen & Rezensionen
Relevanteste Rezensionen
- 09. Apr. 2021
Great printing quality, like brand new. Great value!
Bestätigter Kauf: JaZustand: GebrauchtVerkauft von: erics_place
- 20. Mär. 2019
Good quality.
Bestätigter Kauf: JaZustand: NeuVerkauft von: booksaffordablepenny
- 19. Mär. 2025
Very nice book. High quality....
Bestätigter Kauf: JaZustand: GebrauchtVerkauft von: the_publisher
- 26. Jan. 2016
A good and comprehensive book about machine learning.
Bestätigter Kauf: JaZustand: NeuVerkauft von: mikes-bookstore
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Verkäuferbewertungen (1.945)
- m***- (1038)- Bewertung vom Käufer.Letzte 6 MonateBestätigter Kaufhad a fantastic experience with this vendor-- who i can effortlessly recommend to anybody as both credible & professional in every way-- bona fide across the board, as it were... item was well described & photographed-- pricing was entirely fair & vendor was willing to negotiate with great reasonability-- communication & attention to detail was top-notch as well; finally, my shipment arrived well-packaged & well-ahead of schedule-- inside was a kind note & an unexpected gift-- how thoughtful..!Aztec Mexico Sterling Silver Shot Glass 1.8 Oz HF Brand (Nr. 365450558882)
- n***e (7)- Bewertung vom Käufer.Letzte 6 MonateBestätigter KaufShipping I think took a week or so (which is good). The seller packaged my items extremely well as I had to unwrap a bunch of bubble wrap (which is also good and I will recycle). It is exactly as it was described and shown in the pictures. And it is definitely worth the price as it still functions perfectly and considering there are other seller selling similar items for more than double the price I paid. + the seller helped me work the toy correctly as I had trouble getting it to turn on. 10/10The Claw Machine Pizza Planet Space Crane No Aliens Disney Pixar Toy Story Works (Nr. 365533490147)
- e***i (6)- Bewertung vom Käufer.Letzter MonatBestätigter KaufI would give a 10 out of 10 if I could. This is what ebay is supposed to be. Super friendly, responsive seller. Gorgeous product. Well-packaged. Shipped immediately. Even better than described. Strong value. Apparently the seller added new grips to the clubs, because they are perfect. Truly beautiful golf clubs and I am thrilled to support such a great reseller!