Minimum Enclosing Spheres Formulations for Support Vector Ordinal Regression

Authors:
S. K. Shevade;Wei Chu
Affiliations:
Indian Institute of Science, India;Columbia University, USA
Venue:
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Year:
2006

Citing 0
Cited 3

Simpler core vector machines with enclosing balls

Proceedings of the 24th international conference on Machine learning
Ordinal regression with sparse Bayesian

ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Ordinal-class core vector machine

Journal of Computer Science and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 popularSMO 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.