A Tutorial on Support Vector Machines for Pattern Recognition
Edit Page
Report
Scan day: 07 February 2014 UTC
31
Virus safety - good
Description: By Christopher Burges. 1998. Several formats of document available.
CiteSeerX — A Tutorial on Support Vector Machines for Pattern Recognition A Tutorial on Support Vector Machines for Pattern Recognition (1998) Other Repositories/Bibliography Data Mining and Knowledge Discovery
Size: 212 chars
Contact Information
Email: —
Phone&Fax: —
Address: —
Extended: —
WEBSITE Info
| Page title: | CiteSeerX — A Tutorial on Support Vector Machines for Pattern Recognition |
| Keywords: | CiteSeerX, Christopher J.C. Burges |
| Description: | CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): . 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... |
| IP-address: | 130.203.136.95 |
WHOIS Info
| NS | Name Servers: NS1.PSU.EDU 128.118.25.6 NS2.PSU.EDU 128.118.70.6 |
| WHOIS | |
| Date | activated: 14-Jul-1986 last updated: 22-May-2013 expires: 31-Jul-2014 |