[PDF.44dm] An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Nello Cristianini, John Shawe-Taylor
[PDF.by88] An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
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| #1002352 in Books | Cambridge University Press | 2000-03-28 | Ingredients: Example Ingredients | Original language:English | PDF # 1 | 9.72 x.51 x6.85l,1.26 | File type: PDF | 204 pages | ||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 stan||"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. 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...
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