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Weight-space probability densities and convergence times for stochastic learning

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2 Author(s)
Leen, T.K. ; Dept. of Comput. Sci. & Eng., Oregon Graduate Inst. of Sci. & Technol., Beaverton, OR, USA ; Orr, G.B.

The authors extend the theory of search dynamics for stochastic learning algorithms, address the time evolution of the weight-space probability density and the distribution of convergence times, with particular attention given to escape from local optima, and develop a theoretical framework that describes the evolution of the weight-space probability density. The primary results are exact predictions of the statistical distribution of convergence times for simple backpropagation and competitive learning problems

Published in:

Neural Networks, 1992. IJCNN., International Joint Conference on  (Volume:4 )

Date of Conference:

7-11 Jun 1992