An Introduction to Support Vector Machines and other kernel-based learning methods

von Nello Cristianini ISBN: 0521780195 Kommentar abgeben

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

Excellent introduction
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!
Siehe auch:

• Machine Learning
• Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)
• The Elements of Statistical Learning
• Algorithms on Strings, Trees, and Sequences