An Introduction to Support Vector Machines and Other Kernel-based Learning Methods [Hardcover]

Nello Cristianini (Author), John Shawe-Taylor (Author)

Book Description

ISBN-10: 0521780195 | ISBN-13: 978-0521780193 | Publication Date: March 28, 2000 | Edition: 1

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. 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. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.

Editorial Reviews

Review

"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

Book Description

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.

Product Details

• Hardcover: 204 pages
• Publisher: Cambridge University Press; 1 edition (March 28, 2000)
• Language: English
• ISBN-10: 0521780195
• ISBN-13: 978-0521780193
• Product Dimensions: 10 x 6.8 x 0.6 inches
• Shipping Weight: 1.2 pounds (View shipping rates and policies)
• Average Customer Review: 4.2 out of 5 stars  See all reviews (9 customer reviews)
• Amazon Best Sellers Rank: #607,852 in Books (See Top 100 in Books)
  • #77 in Books > Computers & Technology > Computer Science > Artificial Intelligence > Machine Learning
  • #87 in Books > Professional & Technical > Professional Science > Biological Sciences > Bioinformatics

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Customer Reviews

Most Helpful Customer Reviews

58 of 63 people found the following review helpful:

5.0 out of 5 stars A delightful book to learn support vector machines, April 11, 2000

By 

Random Thoughts (Maryland) - See all my reviews

This review is from: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (Hardcover)

This is a first book introducing support vector learning, a very hot area in machine learning, data mining, and statistics. Aside from Burges (1998)'s tutorial article and Vapnik (1995)'s book, this book by two authors actively working in this field is a welcome addition which is likely to become a standard reference and a textbook among students and researchers who want to learn this important subject. Besides tutoring systematically on the standard theory such as large margin hyperplane, nonlinear kernel classifiers, and support vector regression, this book also deals with growing new areas in this field such as random processes. More interestingly, this book discusses a lot of applications which I consider very imoportant and healthy for the advance of this field, such as medical diagnosis, image analysis, and bioinformatics. In all, I strongly recommend this book for students, and young researchers who want to learn. I'm sure a lot of people will find this book a wise investment, since it provides a handy and timely review of a rapidly growing field.

23 of 25 people found the following review helpful:

4.0 out of 5 stars More for mathematicians than computer scientist, September 20, 2006

By 

Sandro Saitta "www.dataminingblog.com" (Switzerland) - See all my reviews
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Amazon Verified Purchase

This review is from: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (Hardcover)

This book introduces the concepts of kernel-based methods and focuses specifically on Support Vector Machines (SVM). It is hard to read and a good background in mathematic is clearly needed. The book has a strong emphasis on SVM starting from the very first line of text. Concepts are well explained, although equations are not clear. The notation doesn't facilitate the reading at all. The book covers linear as well as kernel learning. The kernel trick is well described. It is easy to understand ideas behind SVM while reading the corresponding chapter. Finally a small chapter on SVM applications is proposed. Unfortunately, it only contains typical SVM applications (i.e. standard problems).

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.

27 of 31 people found the following review helpful:

5.0 out of 5 stars Cogent and Coherent, June 7, 2001

By 

Stephen Gould (Sydney, Australia (sometimes Palo Alto, USA)) - See all my reviews
(REAL NAME)   

This review is from: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (Hardcover)

I used to believe that the thicker the book, the greater the chance that I'd be able to learn something from it. This book by Cristianini and Shawe-Taylor is the complete opposite.

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.