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| View Larger Image | Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)
by Bernhard Schlkopf, Alexander J. Smola
| | List Price: | $75.00 | | Price: | $54.00 | | You Save: | $21.00 (28%) |  | | Available: | Usually ships in 1 to 2 weeks | | | | | Sales Rank: | 53387 | | Studio: | The MIT Press | | | Binding: | Hardcover | | Number Of Pages: | 644 | | Publication Date: | December 15, 2001 | | Publisher: | The MIT Press |
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EDITORIAL REVIEWS |
Product Description
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.
Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years. |
CUSTOMER REVIEWS (Average Customer Rating: 5.0 based on 8 reviews)
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A Bible for Kernel Methods in Machine Learning 
Fantastic book. It is a great, very thorough, introduction to kernel related methods in machine learning. Even if some rare technical passages are not always absolutely clear, it is not a problem because the bibliography contains all the original (recent) articles explaining the concepts. Before buying it, I was not sure all the content would be useful. However as time went by, I realized almost every chapter was of interest to me. Very much worth buying, no other related book comes close.
September 05, 2008 | |
Complete SVM Guide
Excellent theory on SVMs and VC dimensionality. However, I found the chapters on optimization a bit terse. Otherwise, an essential reference for those interested in using SVMs in classification and regression.
February 21, 2008 | |
machine learning via support vector machines and kernels
The authors are young researchers who did their Ph.D. research in this rapidly developing branch of pattern recognition. Because they are young and are at the state of the art in the filed the book has sevral advantages and disadvantages and what I see as a disadvantage someone else might view as an advantage. Anyway here is my view.
Advantage 1: Pattern recognition is a field of many disciplines. It has been studied by statisticians, mathematician, probabilists and engineering and people that call themselves computer scientists specializing in artificial intelligence. The field is old and has a long history but each discipline has developed their own jargon and many times the wheel has been reinvented. The advantage of this book is that these young scientists don't see that awful history. They have learned and mastered their subject in a basically engineering jargon but they include many concepts from statistics and statistical learning theory that are not common to engineering texts. This includes such topics as robust regression, ridge regression and spline estimation. Much of the classical statistical literature is cited. The book contains over 600 references including much of the authors own work.
Disadvantage 1: Because they are young they miss some of the important historical literature and key texts. I found it a little disappointing that the bootstrap which is a statistical tool that has played a major role in discriminant analysis (particularly in the estimation of classification error rates) was completely overlooked. Also although many important texts on pattern recognition, machine learning and discriminant analysis are cited the fine text by McLachlan is overlooked as is the recent relevant text by Hastie, Tibshirani and Friedman.
Advantage 2: This book highlights the work of Vapnik and Chervonenkis and provides nice concise descriptions that one can easily refer to when needed. The mathematics is deep and includes reproducing kernel Hilbert space and many important properties from functional analysis and statistical theory.
Disadvantage 2: The authors are more experienced at writing professional papers than at writing text books. Consequently the book does not flow well and the authors freely admit in their preface that it is best not to read the book in sequential order but rather to take the suggestions in the preface that differ based on the readers background and interest.
Having said all this, for someone like me, who is very knowledgeable about statistical pattern recognition this is a great text for getting me up to speed on an exciting new area that I know very little about. I became curious about it when I started reading Vapnik recently.
I am hoping that a careful reading of this book will give me an intuition about why this approach that incorporates kernel methods can be a powerful tool in pattern recognition and classification.
This book should be a useful reference for anyone interested in this research area. It could be used in an engineering or statistics course in pattern recognition at either the undergraduate or graduate levels depending on what material is covered.
In a recent communication with Bernhard Scholkopf I learned that his book was sent for publication before the Hastie et al. book went to press. So that is the only reason it wasn't referenced. I think that point is worth my mentioning in an editing of this review. Also on reflection I do not think the disadvantages are so great as to remove a star. So it is 5 stars for them.
I can only hope that they will reference the work of McLachlan and Hastie et al. in their future books and research on this subject.
January 23, 2008 | |
Excellent overview of the theory of kernel-based methods
This book is at the right level if you are already strong in Machine Learning theory. (e.g. Tom Mitchell's "Machine Learning").
Note that it is already getting somewhat dated. It for example includes little information on kernels for discreate structured input, such as trees and graphs.
June 21, 2007 | |
In depth review of kernel methods in machine learning 
Great book, but a word of caution, it is not for the novice.
Book assumes a lot of background in functional analysis and
probability. True, it has extensive appendixes but they are
short-handing the relevant materials only. However, having said
that, this is a book worth struggling with even if you have not
yet got the intuitions in the above mentioned disciplines.
It is worthwhile (at least as I can tell) to read the book
skipping the tool chapters (2-6) going back to them when one has
a point where those are needed. I found that to be much easier
as it provides a concrete use of the methods putting them
in context.
October 24, 2005 | |
SIMILAR PRODUCTS |
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