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Deterministic convergence of an online gradient method with momentum

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2006) 4113 LNCS - I 94-105
DOI: 10.1007/11816157_10
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Abstract

An online gradient method with momentum for feedforward neural network is considered. The learning rate is set to be a constant and the momentum coefficient an adaptive variable. Both the weak and strong convergence results are proved, as well as the convergence rates for the error function and for the weight. © Springer-Verlag Berlin Heidelberg 2006.

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Zhang, N. (2006). Deterministic convergence of an online gradient method with momentum. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4113 LNCS-I, pp. 94–105). Springer Verlag. https://doi.org/10.1007/11816157_10

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