【文件属性】:
文件名称:A Tutorial on Support Vector Machines for Pattern
文件大小:250KB
文件格式:RAR
更新时间:2012-07-19 17:58:55
SVM
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 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 find that even old material is cast in a fresh light.
【文件预览】:
aybook.cn_svm_tutourial.pdf