A Novel Ordinal Regression Method with Minimum Class Variance Support Vector Machine

DOI

10.2991/meita-15.2015.167How to use a DOI?

Keywords

Machine learning, Ordinal regression, Support vector machine, Support vector ordinal regression.

Abstract

In the paper, we propose a novel ordinal regression method called minimum class variance support vector ordinal regression (MCVSVOR). MCVSVOR is derived from minimum class variance support vector machine (MCVSVM) which is a variant of SVM, and so inherits the latter’s characteristics such as taking the distribution of the categories into consideration and good generalization performance. Finally, the experimental results validate the effectiveness of MCVSVOR and indicate its superior generalization performance over SVOR.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jinrong Hu
AU  - Xiaoming Wang
AU  - Zengxi Huang
PY  - 2015/08
DA  - 2015/08
TI  - A Novel Ordinal Regression Method with Minimum Class Variance Support Vector Machine
BT  - Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications
PB  - Atlantis Press
SP  - 894
EP  - 898
SN  - 2352-5401
UR  - https://doi.org/10.2991/meita-15.2015.167
DO  - 10.2991/meita-15.2015.167
ID  - Hu2015/08
ER  -