Shevade, SK and Chu, Wei (2006) Minimum enclosing spheres formulations for support vector ordinal regression. In: 6th IEEE International Conference on Data Mining,, Dec 18-22, 2006, Hong Kong, Peoples R China, pp. 1054-1058.
04053152.pdf - Published Version
Restricted to Registered users only
Download (193Kb) | Request a copy
We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. Both approaches guarantee that the radii of the spheres are properly ordered at the optimal solution. The size of the optimization problem is linear in the number of training samples. The popular SMO algorithm is adapted to solve the resulting optimization problem. Numerical experiments on some real-world data sets verify the usefulness of our approaches for data mining.
|Item Type:||Conference Paper|
|Additional Information:||Copyright 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||01 Sep 2010 05:48|
|Last Modified:||02 Nov 2011 05:58|
Actions (login required)