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The Bayesian Committee Support Vector Machine

  • Anton Schwaighofer
  • Volker Tresp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2130)

Abstract

Empirical evidence indicates that the training time for the support vector machine (SVM) scales to the square of the number of training data points. In this paper, we introduce the Bayesian committee support vector machine (BC-SVM) and achieve an algorithm for training the SVM which scales linearly in the number of training data points. We verify the good performance of the BC-SVM using several data sets.

Keywords

Support Vector Machine Query Point Training Point Relevance Vector Machine Gaussian Process Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Anton Schwaighofer
    • 1
    • 2
  • Volker Tresp
    • 2
  1. 1.Institute for Theoretical Computer ScienceTU GrazGrazAustria
  2. 2.Corporate TechnologySiemens AGMünchenGermany

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