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Abstract: A parallel stochastic algorithm is investigated for error-descent learning and optimization in deterministic networks of arbitrary topology. No explicit information about internal network structure is needed. The method is based on the model-free distributed learning mechanism of Dembo and Kailath. A modified parameter update rule is proposed by which each individual parameter vector perturbation contributes a decrease in error. A substantially faster learning speed is hence allowed.... (Update)
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.... Perturbation In weight perturbation (Jabri and Flower, 1991; Alspector et al. 1993; Flower and Jabri, 1993; Kirk et al. 1993; Cauwenberghs, 1993) the gradient r w is approximated using only the globally broadcast result of the computation of E(w) This is done by adding a...
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BibTeX entry: (Update)
Gert Cauwenberghs. A fast stochastic error-descent algorithm for supervised learning and optimization. In S.J Hanson, J.D. Cowan, and C.L. Giles, editors, Advances in Neural Information Processing Systems 5, pages 244--251, San Mateo, CA, 1993. Morgan Kaufmann Publishers. http://citeseer.ist.psu.edu/cauwenberghs93fast.html More
@inproceedings{ cauwenberghs93fast,
author = "Gert Cauwenberghs",
title = "A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization",
booktitle = "Advances in Neural Information Processing Systems",
volume = "5",
publisher = "Morgan Kaufmann, San Mateo, CA",
editor = "Stephen Jos{\'e} Hanson and Jack D. Cowan and C. Lee Giles",
pages = "244--251",
year = "1993",
url = "citeseer.ist.psu.edu/cauwenberghs93fast.html" }
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