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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods [Kindle Edition]

Nello Cristianini , John Shawe-Taylor
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This slim book is an excellent introduction to an exciting new field--the design and implementation of important new mathematical models as the optimising strategy for learning machines. The text deals clearly with both the concepts and the practical details of these models, and consistently steers away from the more speculative side of learning machines. At the same time, the authors never fail to communicate the wonder and excitement of working with the raw stuff of knowledge and impart a spark of intelligence to mechanisms.

An Introduction to Support Vector Machines is manifestly a text book, and as such leads the reader as a student through the concepts, history and implementation of kernel-based learning strategies, with plenty of pseudo-code examples, discussion and exercise questions (without answers), and the best modern bibliography of the subject available. The appendix attempts to summarise the background mathematics. It's thorough, accurate and useful as a reference: but it is not a tutorial, and may leave the novice reader with little training in set dynamics none the wiser. A well-rounded reader will sail through the intriguing first chapter, which discusses learning machines and techniques for teaching a machine (or computer program) to generalise. However chapter two, with its impenetrable conversation about "linear classification" replete with sigma notation and diagrams of "hyperplanes" may well discourage further reading. Excellent though this book is, the title is deceptive: it is indeed an "introduction"--but requires a fair background knowledge of automata and set theory.--Wilf Hey


'… 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


  • Format: Kindle Edition
  • Dateigröße: 2897 KB
  • Seitenzahl der Print-Ausgabe: 208 Seiten
  • ISBN-Quelle für Seitenzahl: 0521780195
  • Gleichzeitige Verwendung von Geräten: Bis zu 4 Geräte gleichzeitig, je nach vom Verlag festgelegter Grenze
  • Verlag: Cambridge University Press; Auflage: 1 (23. März 2000)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • ASIN: B00AKE1PR8
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Word Wise: Nicht aktiviert
  • Verbesserter Schriftsatz: Nicht aktiviert
  • Durchschnittliche Kundenbewertung: 5.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)
  • Amazon Bestseller-Rang: #567.991 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)

  •  Ist der Verkauf dieses Produkts für Sie nicht akzeptabel?


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6 von 8 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Excellent introduction 11. Januar 2001
Format:Gebundene Ausgabe
For those who want to start working with support vectors machine, this is a concise, clear, and authoritative introduction. Brief historiographic-bibliographic sections at the end of each chapter help figuring out how concepts emerged in the literature. Recommendable!
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Die hilfreichsten Kundenrezensionen auf (beta) 4.2 von 5 Sternen  10 Rezensionen
63 von 69 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen A delightful book to learn support vector machines 12. April 2000
Von Abstract Space - Veröffentlicht auf
Format:Gebundene Ausgabe
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.
35 von 38 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen More for mathematicians than computer scientist 20. September 2006
Von Sandro Saitta - Veröffentlicht auf
Format:Gebundene Ausgabe|Verifizierter Kauf
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.
31 von 35 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Cogent and Coherent 8. Juni 2001
Von Stephen Gould - Veröffentlicht auf
Format:Gebundene Ausgabe
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.
6 von 6 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Very good at exactly what it is - a book ONLY about Kernel-Based Learning 10. April 2009
Von Craig Garvin - Veröffentlicht auf
Format:Gebundene Ausgabe
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.
110 von 162 Kunden fanden die folgende Rezension hilfreich
1.0 von 5 Sternen Not even close to an intro... 20. März 2004
Von Amazon Customer - Veröffentlicht auf
Format:Gebundene Ausgabe
Oh Puhleeeezzzzz... How is your vector math??? Remember your linear algebra well? Do you have a background in SVM's? Intuitively able to suck out of thin air the meaning of the Gamma co-efficient as applied to svm's?? You've read all the background papers and remember your formal logic???? No?? too bad..your out of luck..

This book is more aptly titled an Introduction to the Formalisms of SVM's. If your a software engineer trying to implement one of these, forget it.. Be nice if they put that quadratic algorthim psuedocode into something more readable than greek symbology..

If you are trying to build one of these engines, then this book is of absolutely no help, unless you have a background in machine learning and have read all the papers on SVM's. If you can decompose the math into code in your head, then you might find it entertaining... What I don't get is how all the rest of these reviewers can give such "glowing praise" for this book and have it be so completely worthless as an introduction... makes me think some of these are shills..

Bottom line is, if your trying to code a svm, this book will not help. If your trying to understand how to implement a svm, this book will not help. If you are trying to understand how an svm works, this book will not help. If you want to know the mathematical basis for SVM's and like that presentation.. this is the book for you..
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