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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods 1st Edition

4.2 out of 5 stars 10 customer reviews
ISBN-13: 978-0521780193
ISBN-10: 0521780195
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  • An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
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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.
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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: 6.8 x 0.5 x 9.7 inches
  • Shipping Weight: 1.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (10 customer reviews)
  • Amazon Best Sellers Rank: #702,596 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: 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.
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Format: Hardcover Verified Purchase
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.
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Format: 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.
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Format: Hardcover
We incorporated a Support Vector Machine Classifier in our analysis software product. Although other texts and articles provided friendlier background and an easier introduction, when the time came to actually code a classifier, this was the book that offered the level of detail required to build something that ran. The math is heavy, the prose is terse, but it goes deep under the covers of what actually constitutes a kernel transformation, what function families qualify as kernels, as well as deep component-by-component algorithms.

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.
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Format: Hardcover Verified Purchase
This textbook does an excellent job developing the mathematics and providing intuition behind SVMs. That being said, This textbook assumes you have a firm grasp on vector calculus and linear algebra. Chapters 1 and 2 provide a good overview of how machine learning can be used in classification and linear regression. Chapter 5 is an overview of what is covered in most introductory multivariate calculus classes. However, chapter 3 and chapter 4 are not easy. If you lack a background in real analysis you can read the first four or five pages of each chapter and get a basic idea of what is being discussed. Some of the proofs could use a bit more explanation, but at the same time they are mathematically rigorous and concise.

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.
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