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Globally convergent algorithms with local learning rates

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3 Author(s)
G. D. Magoulas ; Dept. of Inf. Syst. & Comput., Brunel Univ., London, UK ; V. P. Plagianakos ; M. N. Vrahatis

A novel generalized theoretical result is presented that underpins the development of globally convergent first-order batch training algorithms which employ local learning rates. This result allows us to equip algorithms of this class with a strategy for adapting the overall direction of search to a descent one. In this way, a decrease of the batch-error measure at each training iteration is ensured, and convergence of the sequence of weight iterates to a local minimizer of the batch error function is obtained from remote initial weights. The effectiveness of the theoretical result is illustrated in three application examples by comparing two well-known training algorithms with local learning rates to their globally convergent modifications

Published in:

IEEE Transactions on Neural Networks  (Volume:13 ,  Issue: 3 )