Multitask twin support vector machines

Authors:
Xijiong Xie;Shiliang Sun
Affiliations:
Department of Computer Science and Technology, East China Normal University, Shanghai, P.R. China;Department of Computer Science and Technology, East China Normal University, Shanghai, P.R. China
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Year:
2012

Citing 11
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Abstract

Multitask learning is a learning paradigm which seeks to improve the generalization performance of a task with the help of other tasks. Learning multiple related tasks simultaneously has been empirically as well as theoretically shown to improve performance relative to learning each task independently. In this paper, we propose a new classification method named multitask twin support vector machines based on the regularization principle and twin support vector machines. Our new approach is that we put twin support vector machines to multitask learning. Experimental results demonstrate that the proposed method dramatically improves the performance relative to learning each task independently.