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Online Multiple Kernel Learning: Algorithms and Mistake Bounds

  • Rong Jin
  • Steven C. H. Hoi
  • Tianbao Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6331)

Abstract

Online learning and kernel learning are two active research topics in machine learning. Although each of them has been studied extensively, there is a limited effort in addressing the intersecting research. In this paper, we introduce a new research problem, termed Online Multiple Kernel Learning (OMKL), that aims to learn a kernel based prediction function from a pool of predefined kernels in an online learning fashion. OMKL is generally more challenging than typical online learning because both the kernel classifiers and their linear combination weights must be learned simultaneously. In this work, we consider two setups for OMKL, i.e. combining binary predictions or real-valued outputs from multiple kernel classifiers, and we propose both deterministic and stochastic approaches in the two setups for OMKL. The deterministic approach updates all kernel classifiers for every misclassified example, while the stochastic approach randomly chooses a classifier(s) for updating according to some sampling strategies. Mistake bounds are derived for all the proposed OMKL algorithms.

Keywords

On-line learning and relative loss bounds Kernels 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rong Jin
    • 1
  • Steven C. H. Hoi
    • 2
  • Tianbao Yang
    • 1
  1. 1.Department of Computer Science and EngineeringMichigan State UniversityUSA
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingapore

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