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A Multi-view Regularization Method for Semi-supervised Learning

  • Jiao Wang
  • Siwei Luo
  • Yan Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6063)

Abstract

Multi-view semi-supervised learning is a hot research topic recently. In this paper, we consider the regularization problem in multi-view semi-supervised learning. A regularization method adaptive to the given data is proposed, which can use unlabeled data to adjust the degree of regularization automatically. This new regularization method comprises two levels of regularization simultaneously. Experimental evidence on real word dataset shows its effectivity.

Keywords

multi-view learning semi-supervised learning regularization  machine learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jiao Wang
    • 1
  • Siwei Luo
    • 1
  • Yan Li
    • 1
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina

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