A tutorial on support vector machines for pattern recognition-英文文献.pdf

c Kluwer Academic Publishers Boston Manufactured in The Netherlands A Tutorial on Supp ort Vector Machines for Pattern Recognition CHRISTOPHER JC BURGES burgeslucentcom Bel l Laboratories Lucent Technologies Editor Usama Fayyad 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 nonseparable data working through a nontrivial 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 innite 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 accuracy of SVMs which we review Results of some experiments which were inspired by these arguments are also presented We give numerous examples and proofs of most of the key theorems There is new material and I hope that the reader will nd that even old material is cast in a fresh light Keywords Support Vector Machines Statistical Learning Theory VC Dimension Pattern Recognition Intro duction The purpose of this paper is to provide an introductory yet extensive tutorial on the basic ideas behind Support Vector Machin