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Binding: Hardcover
Dewey Decimal Number: 006.4
EAN: 9780387310732
Edition: 1
ISBN: 0387310738
Label: Springer
Manufacturer: Springer
Number Of Items: 1
Number Of Pages: 738
Publication Date: October 01, 2007
Publisher: Springer
Studio: Springer

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Alternate Versions: Click to Display

Accessories:

• Pixelization Paradigm: Visual Information Expert Workshop, VIEW 2006, Paris, France, April 24-25, 2006, Revised Selected Papers (Lecture Notes in Computer Science)
• Multimodal Technologies for Perception of Humans: First International Evaluation Workshop on Classification of Events, Activities and Relationships, CLEAR ... Papers (Lecture Notes in Computer Science)

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Editorial Review:

Product Description:


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.



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






Rating: - Little emphasis on concepts
After reading "Pattern Recognition using Neural Networks" written by the same author, I was expecting a book of the same league: strong emphasis on the conceptual foundations, and a distillation of the great ideas of a field that is enjoying a great deal of research.

However, I was greatly disappointed. While it is certainly not an easy task, the author makes no attempt to extract a unifying conceptual framework that underlies the vastly disperse approaches in the scientific literature. There are no powerful "take-home messages" other than "Bayes' Rule". Instead, it is written as a reference book, where task-specific algorithms are presented in an almost isolated form---and thus this text ends up baring certain similarity to a cookbook.

Conclusion: This books succeeds as a reference book (fairly complete, biased towards Bayesian methods), but it is not a book about the foundations of Machine Learning.

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Rating: - Probably the best book for machine learning
I am a PhD student in machine learning. Bishop is really gifted and he explains very well basic and advanced concepts of machine learning. I would say that this book is much more comprehensive than Hastie's Statistical learning book The Elements of Statistical Learning. Very good illustrations and very complete. I would definitely recommend it for those who want to learn statistical/machine learning on their own. The only thing that I don't like is that it often tries to present theories under a probabilistic framework. My personal opinion is that Probabilities is a nice way to present easy theories in a super complex way. I tend to think in a more linear algebra/optimization framework. After so many years in Academia probabilities still confuse me. Hopefully the author doesn't use only the probabilistic framework, that is why I like it

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Rating: - concentrates too much on the easy stuff
The book is worth a look, but after some of 5 star reviews i read here, it was quite a disappointment. Yes, the book covers a lot of ground. Yes, the book has lots of nice pictures and easy examples, but that is exactly the problem. There are lots and lots of simple examples to explain the most basic concepts, but when it gets complicated the book often sounds as if the text was taken out of a mathematics book. For example: the basics of probability theory are introduced for over 5 pages with the example of "two coloured boxes each containing fruit". Nothing wrong with that. Then the chapter continues with probability densities which are covered within 2 pages and contain sentences like "Under a nonlinear change of variable, a probability density transforms differently from a simple function, due to the Jacobian factor". There is no mentioning how a simple function exactly transforms, what a Jacobian factor actually is and why we would be interested in a nonlinear change. Surely, some of ... Read More

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Rating: - Authorative text
I am a PhD student who wanted to own a good book on pattern recognition. I asked my professor, who had recently attended an international conference on speech recognition, which book to buy. He said that several top academics in the field at the conference had agreed that this was THE book to have, and he agrees with them.

After reading though the first few chapters I am impressed by the structured way concepts are related. I like that the basic probability theory needed to understand the concepts are recapped and explained in an understandable way.

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Rating: - Awesome
Start right from the first page. No gimmicks. Plain old mathematics and useful stuff, then to machine learning. You always know, the rationale behind the chapters or the sentence. Very inspiring.

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