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Development and convergence analysis of training algorithms with local learning rate adaptation

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Development and convergence analysis of training algorithms with local learning rate adaptation
Magoulas, GD Plagianakos, VP Vrahatis, MN
A new theorem for the development and convergence analysis of supervised training algorithms with an adaptive learning rate for each weight is presented. Based on this theoretical result, a strategy is proposed to automatically adapt the search direction, as well as the stepsize length along the resultant search direction. This strategy is applied to some well known local learning algorithms to investigate its effectiveness. (EN)
scientific_publication_inproceedings
Επιστημονική δημοσίευση - Ανακοίνωση Συνεδρίου (EL)
Scientific publication - Conference Paper (EN)
University of Athens
Pergamos Digital Library
Conference Paper
English
2000-01-01
https://pergamos.lib.uoa.gr/uoa/dl/object/uoadl:3052733
uoadl:3052733
IEEE Comput. Soc (EL)
IEEE Comput. Soc (EN)



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