PMID: 9427104Jun 1, 1997Paper

Symmetry breaking and training from incomplete data with Radial Basis Boltzmann Machines

International Journal of Neural Systems
M J Nijman, H J Kappen

Abstract

A Radial Basis Boltzmann Machine (RBBM) is a specialized Boltzmann Machine architecture that combines feed-forward mapping with probability estimation in the input space, and for which very efficient learning rules exist. The hidden representation of the network displays symmetry breaking as a function of the noise in the dynamics. Thus, generalization can be studied as a function of the noise in the neuron dynamics instead of as a function of the number of hidden units. We show that the RBBM can be seen as an elegant alternative of k-nearest neighbor, leading to comparable performance without the need to store all data. We show that the RBBM has good classification performance compared to the MLP. The main advantage of the RBBM is that simultaneously with the input-output mapping, a model of the input space is obtained which can be used for learning with missing values. We derive learning rules for the case of incomplete data, and show that they perform better on incomplete data than the traditional learning rules on a 'repaired' data set.

References

Jan 1, 1978·Comparative Biochemistry and Physiology. C: Comparative Pharmacology·M J Doughty
Aug 20, 1990·Physical Review Letters·K RoseG C Fox

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Citations

Aug 10, 2000·Neural Computation·S Akaho, H J Kappen
Mar 30, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Chee-Peng LimMei-Ming Kuan
Jul 9, 2003·IEEE Engineering in Medicine and Biology Magazine : the Quarterly Magazine of the Engineering in Medicine & Biology Society·Noël L KeijsersStan C Gielen
May 10, 2016·Accountability in Research·Logan M SteeleMichael D Mumford

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