- Hardcover: 204 pages
- Publisher: Cambridge University Press; 1 edition (March 28, 2000)
- Language: English
- ISBN-10: 0521780195
- ISBN-13: 978-0521780193
- Product Dimensions: 6.8 x 0.6 x 9.7 inches
- Shipping Weight: 1.2 pounds (View shipping rates and policies)
- Average Customer Review: 10 customer reviews
- Amazon Best Sellers Rank: #834,477 in Books (See Top 100 in Books)
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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods 1st Edition
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"This book is an excellent introduction to this area... it is nicely organized, self-contained, and well written. The book is most suitable for the beginning graduate student in computer science." Richard A Chechile, Journal of Mathematical Psychology
This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications.
Top customer reviews
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I think this book is good if you:
* Have a strong mathematical background
* Work in the specific domain of SVM (or kernel-based methods in general)
* Want to write a research paper about SVM and need the correct notations
However, this book is NOT intended for people who:
* Don't like to read theorems, corollaries and remarks
* Are not interested in reading hundreds of proofs
This is my personal opinion as a computer scientist: this book is definitely written for mathematicians.
Lastly, the author makes mention that you can read the book out of order. Here's the order that I read: chapter 1, chapter 2, chapter 5, chapter 3, chapter 6, chapter 4, chapter 8, and then chapter 7. I think this order gave me enough background information and motivated me to delve into the mathematical theory. I just want finish up by saying that if you're looking for a support vector implementation guide this is certainly not for you.
My only real criticism of the book is that the authors sometimes resort to wacky matrix notation to get mathematical expressions to fit inline, when they really should be centered and displayed in full.
I think the best addition would be a prologue entitled "How To Read This Book," because it seems that other reviewers are dissatisfied by the lack of hand-holding required for someone with none of the (prerequisite) comfort level with linear algebra notation and methods of proof. That being said, almost all of the proofs contained in the book are there for completeness alone. One does not need to reprove each proposition in order to understand how to implement an SVM or grasp the concepts behind kernel-based methods. For instance, the KKT conditions are essential as a measure of convergence in training an SVM, but the derivation is superfluous for an engineer.
As an analogy, just because a book on algorithms presents bubble sort doesn't make bubble sort important, but omitting bubble sort (or some other introductory sorting algorithm) would make an introductory volume incomplete. The authors here provide the same foundations for support vector machines, so that the reader can actually understand why it works. This book is self-contained, and it's much better for it.
In my search for a good book on SVMs, this one was by far superlative. I would recommend it to anyone in my position who is interested in mathematics and programming.
The biggest drawback of this book is that it does not meet the needs of the many non-mathematically inclined who are interested in SVM's. It uses the academic euphemism 'introduction' to mean 'brutally advanced, but if I called it that, no one would buy it'. One of the reviewers was expecting an actual introduction, and was disappointed.