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A New Learning Algorithm for Mean Field Boltzmann Machines

  • Max Welling
  • Geoffrey E. Hinton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2415)

Abstract

We present a new learning algorithm for Mean Field Boltzmann Machines based on the contrastive divergence optimization criterion. In addition to minimizing the divergence between the data distribution and the equilibrium distribution, we maximize the divergence between one-step reconstructions of the data and the equilibrium distribution. This eliminates the need to estimate equilibrium statistics, so we do not need to approximate the multimodal probability distribution of the free network with the unimodal mean field distribution. We test the learning algorithm on the classification of digits.

Keywords

Independent Component Analysis Hide Unit Coordinate Descent Contrastive Divergence Boltzmann Machine 
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 2002

Authors and Affiliations

  • Max Welling
    • 1
  • Geoffrey E. Hinton
    • 1
  1. 1.Depart. of Computer ScienceUniv. of TorontoTorontoCanada

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