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Convergence analysis of stochastic pseudo-gradient algorithms and application to learning in feedforward neural networks

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2 Author(s)
V. Tadic ; Autom. Control Lab., Mihajlo Pupin Inst., Belgrade, Serbia ; S. Stankovic

The convergence of a class of stochastic pseudo-gradient algorithms driven by correlated data sequences is considered in this paper. The obtained results are applied to a learning algorithm for feedforward neural networks and sufficient conditions for its convergence are determined

Published in:

Information Theory. 1997. Proceedings., 1997 IEEE International Symposium on

Date of Conference:

29 Jun-4 Jul 1997