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Multitask Twin Support Vector Machines

  • Xijiong Xie
  • Shiliang Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)

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.

Keywords

Multitask learning Regularization principle Twin support vector machines 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xijiong Xie
    • 1
  • Shiliang Sun
    • 1
  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiP.R. China

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