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Accession Number:
ADP007159
Title:
Note on Learning Rate Schedules for Stochastic Optimization,
Corporate Author:
YALE UNIV NEW HAVEN CT DEPT OF COMPUTER SCIENCE
Report Date:
1992-01-01
Abstract:
We present and compare learning rate schedules for stochastic gradient descent, a general algorithm which includes LMS, on-line back-propagation and k-means clustering as special cases. We introduce search-then-converge type schedules which outperform the classical constant and running average lt schedules both in speed of convergence and quality of solution.
Supplementary Note:
This article is from 'Computing Science and Statistics: Proceedings of the Symposium on the Interface Critical Applications of Scientific Computing: Biology, Engineering, Medicine, Speech Held in Seattle, Washington on 21-24 April 1991,' AD-A252 938, p313-317.
Pages:
0005
File Size:
0.00MB
