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Pattern Recognition and Machine Learning习题答案（英文）

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Pattern Recognition and Machine Learning

Solutions to the Exercises: Tutors’ Edition

Markus Svens

en and Christopher M. Bishop

 2002–2009

This is the solutions manual (Tutors’ Edition) for the book Pattern Recognition and Machine Learning

(PRML; published by Springer in 2006). This release was created September 8, 2009. Any future releases

(e.g. with corrections to errors) will be announced on the PRML web-site (see below) and published via

Springer.

PLEASE DO NOT DISTRIBUTE

Most of the solutions in this manual are intended as a resource for

tutors teaching courses based on PRML and the value of this resource

would be greatly diminished if was to become generally available. All

tutors who want a copy should contact Springer directly.

The authors would like to express their gratitude to the various people who have provided feedback on

earlier releases of this document.

The authors welcome all comments, questions and suggestions about the solutions as well as reports on

(potential) errors in text or formulae in this document; please send any such feedback to

prml-fb@microsoft.com

Further information about PRML is available from

http://research.microsoft.com/∼cmbishop/PRML

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Pattern Recognition and Machine Learning(完整习题答案)

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Pattern Recognition and Machine Learning

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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 by instructors 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.

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