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

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

   

AUTHORS: 

  • Nello Cristianini
  • John Shawe-Taylor

PRODUCT DETAILS:

  • Hardcover: 189 pages
  • Publisher: Cambridge University Press; 1st edition (March 28, 2000)
  • Language: English
  • ISBN: 0521780195
  • Product Dimensions: 10.0 x 6.8 x 0.6 inches
  • Shipping Weight: 1.26 pounds
EDITORIAL REVIEWS

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. 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.

Book Info

First comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Text and associated web site will help readers understand the theory and its real-world applications, such as text categorization, handwritten character recognition, etc. For students and practitioners.

CUSTOMER REVIEWS

A delightful book to learn support vector machines, April 11, 2000 

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.


Rating: not rated | Added on: 5 Nov 2006

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