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. 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 ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.
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Revue de presse
'… the most accessible introduction to the area I have yet seen'. D. J. Hand, Publication of the International Statistical Institute
'The book is an admirable presentation of this powerful new approach to pattern classification.' Alex M. Andrew, Robotica
' … an excellent book, complete and readable without big requirements in mathematical functional analysis.' Zentralblatt für Mathematik und ihre Grenzgebiete Mathematics Abstracts
'The book is an admirable presentation of this powerful new approach to pattern classification.' Alex M. Andrew, Robotica
' … an excellent book, complete and readable without big requirements in mathematical functional analysis.' Zentralblatt für Mathematik und ihre Grenzgebiete Mathematics Abstracts
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Couverture | Copyright | Table des matières | Extrait | Index | Quatrième de couverture
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Couverture | Copyright | Table des matières | Extrait | Index | Quatrième de couverture
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0 internautes sur 1 ont trouvé ce commentaire utile
Par Un client
Format:Relié
Cet ouvrage donne un bon appercu des SVM, bien que pas tres accessible pour un neophyte.
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4.2 étoiles sur 5
9 commentaires
60 internautes sur 65 ont trouvé ce commentaire utile
5.0 étoiles sur 5
A delightful book to learn support vector machines
12 avril 2000
Par Random Thoughts
- Publié sur Amazon.com
Format:Relié
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.
28 internautes sur 30 ont trouvé ce commentaire utile
4.0 étoiles sur 5
More for mathematicians than computer scientist
20 septembre 2006
Par Sandro Saitta
- Publié sur Amazon.com
Format:Relié|Achat authentifié par Amazon
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.
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.
29 internautes sur 33 ont trouvé ce commentaire utile
5.0 étoiles sur 5
Cogent and Coherent
8 juin 2001
Par Stephen Gould
- Publié sur Amazon.com
Format:Relié
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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4.2 étoiles sur 5
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