A Tutorial on Support Vector Machines for Pattern Recognition

A Tutorial on Support Vector Machines for Pattern Recognition (1998)

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by Christopher J.C. Burges

Venue:	Data Mining and Knowledge Discovery
Citations:	1656 - 11 self

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Datum	Value	Source
TITLE	A Tutorial on Support Vector Machines for Pattern Recognition	user correction - Legacy Corrections
AUTHOR NAME	Christopher J.C. Burges	user correction - Legacy Corrections
AUTHOR AFFIL	Bell Laboratories, Lucent Technologies	user correction - Legacy Corrections
AUTHOR ADDR	Editor: Usama Fayyad	user correction - Legacy Corrections
ABSTRACT	. The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accura...	user correction - Legacy Corrections
YEAR	1998	INFERENCE
VENUE	Data Mining and Knowledge Discovery	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	121--167	INFERENCE
VOLUME	2	INFERENCE
CITATIONS	58 found	ParsCit 1.0