- 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 1.1 x 10 inches
- Shipping Weight: 3.3 pounds (View shipping rates and policies)
- Average Customer Review: 14 customer reviews
- Amazon Best Sellers Rank: #798,293 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)
About the Author
Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.
Top customer reviews
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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 :)
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.
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.
I started out dabbling with SVM, and turned to this book when I really needed to know what was going on. I found everything that I needed, and much more. Having a Ph.D. in mathematics helped with that side of the text, but found myself a bit overwhelmed with the statistics, a field that has numerous technical terms. The appendix on stats was insufficient. And learning theory was completely new to me. But as it turned out, neither was essential to understanding the rest of the book. The book has numerous examples, and the real learning happens there. I had fun reading the book, but my idea of fun is something most people would away from, screaming.
The book has a gigantic bibliography, and the authors constantly refer to it. If you want to explore further in any particular direction, you will know where to start looking.
There are a few typos, as can be expected in a book this large, and one theorem that is false as stated. (Theorem 4.1 needs the hypothesis of convexity added.) But the theorem is never actually used; it is just an illustration. One thing that I would have liked is some Web site for the book where these things can be brought up.
1. Linear & Matrix Algebra
2. Functional analysis, (derivatives, gradients,...)
3. Optimization Theory (min ,max problems, convex optimization, unconstrained ,constrained optimizations)
4. Probability theory ( Bayes rule,and others like PDF ...)
Probably, the book will be a challenge for those who hasn't majored in applied mathematics. If you think, you don't have enough background, at a time you will get stuck in understanding a theorem.
Overall, it is the unique book that provides in depth analysis and explanation of kernel methods and support vector machines.
Most recent customer reviews
Book assumes a lot of background in functional analysis and