- 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 0.5 x 9.7 inches
- Shipping Weight: 1.2 pounds (View shipping rates and policies)
- Average Customer Review: 3.9 out of 5 stars See all reviews (10 customer reviews)
- Amazon Best Sellers Rank: #417,701 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
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.
The book is clear and concise in it's development of the theory of SVMs, and is thorough in going through all relevant background material. Particularly useful is the section optimisation which is usually missing from statistical and computer science backgrounds.
Beware that this book is not for the mathematically shy. If you want to learn about SVMs and don't mind getting your teeth stuck into some serious (applied) maths, then this book is for you.
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.
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.
Most Recent Customer Reviews
You can see my write up there at the link below:
I liked the book overall.