Read An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Nello Cristianini, John Shawe-Taylor Online
Read [Nello Cristianini, John Shawe-Taylor Book] ^ An Introduction to Support Vector Machines and Other Kernel-based Learning Methods Online ! PDF eBook or Kindle ePUB free. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods A delightful book to learn support vector machines 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 systematic
| Title | : | An Introduction to Support Vector Machines and Other Kernel-based Learning Methods |
| Author | : | |
| Rating | : | 4.71 (871 Votes) |
| Asin | : | 0521780195 |
| Format Type | : | paperback |
| Number of Pages | : | 204 Pages |
| Publish Date | : | 2014-01-05 |
| Language | : | English |
A delightful book to learn support vector machines 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 process. More for mathematicians than computer scientist 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. "Happy with SVM intro" according to Stuart M. Rodgers. I wrote my review of this book on the ai forum.You can see my write up there at the link below:[]I liked the book overall.
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.. This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory
"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
Download An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Download as PDF : Click Here
Download as DOC : Click Here
Download as RTF : Click Here