- Series: Adaptive Computation and Machine Learning Series
- Hardcover: 644 pages
- Publisher: The MIT Press; 1st edition (December 15, 2001)
- Language: English
- ISBN-10: 0262194759
- ISBN-13: 978-0262194754
- Product Dimensions: 8 x 1.3 x 10 inches
- Shipping Weight: 3.3 pounds (View shipping rates and policies)
- Average Customer Review: 15 customer reviews
- Amazon Best Sellers Rank: #271,442 in Books (See Top 100 in Books)
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) 1st Edition
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"Interesting and original. Learning with Kernels will make a fine textbook on this subject."--Grace Wahba, Bascom Professor of Statistics, University of Wisconsin Madison "This splendid book fills the need for a comprehensive treatment of kernel methods and support vector machines. It collects results, theorems, and discussions from disparate sources into one very accessible exposition. I am particularly impressed that the authors have included problem sets at the end of each chapter; such problems are not easy to construct, but add significantly to the value of the book for the student audience."--Chris J. C. Burges, Microsoft Research
Interesting and original. Learning with Kernels will make a fine textbook on this subject.―Grace Wahba, Bascom Professor of Statistics, University of Wisconsin Madison (Endorsement)
This splendid book fills the need for a comprehensive treatment of kernel methods and support vector machines. It collects results, theorems, and discussions from disparate sources into one very accessible exposition. I am particularly impressed that the authors have included problem sets at the end of each chapter; such problems are not easy to construct, but add significantly to the value of the book for the student audience.―Chris J. C. Burges, Microsoft Research (Endorsement)
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One may need an intermediate level of mathematics and linear algebra to understand the derivations and kernel designing ideas.
This book presents a very deep mathematics to backup the theory of SVMs. This includes reproducing kernel Hilbert spaces, functional analysis and probability. If you are not familiar with these concepts already, you probably are not ready for this book.
The appendices are great references, assuming you already have the necessary background.
I find this book rather deep in learning with kernels and i suggest it as a reading for starting phD (and master?) students to get in grasp with concepts. And then delve into more details.
The book itself is nicely organized by topics and is not at all to be read chapter by chapter. Authors give some guidelines what to start according to your prior knowledge or experience.
I take one star off to show that "may be" there was a way to write about the same complex and fast developing domain better and easier to grasp for a reader :)
The best thing is that after finishing one or two basic chapters, you can read the rest of the book in any order; most chapters are almost independent to each other. At the beginning of a chapter, the authors list the prerequistites, so a reader knows whether he will be able to understand the chapter.
For now the book still reflects the state of art. But it is a fast changing field. I hope the authors will update the book in the future.
Learning With Kernels is the best book I have found on the subject. If you want to really understand Kernels you should learn the math just to read this book.
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.
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