Download An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, by Nello Cristianini, John Shawe-Taylor
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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, by Nello Cristianini, John Shawe-Taylor
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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.
- Sales Rank: #899965 in Books
- Brand: Brand: Cambridge University Press
- Published on: 2000-03-28
- Original language: English
- Number of items: 1
- Dimensions: 9.72" h x .51" w x 6.85" l, 1.26 pounds
- Binding: Hardcover
- 204 pages
- Used Book in Good Condition
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
Most helpful customer reviews
63 of 69 people found the following review helpful.
A delightful book to learn support vector machines
By Abstract Space
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.
36 of 39 people found the following review helpful.
More for mathematicians than computer scientist
By Sandro Saitta
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.
6 of 6 people found the following review helpful.
Very good at exactly what it is - a book ONLY about Kernel-Based Learning
By Craig Garvin
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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