Fast curvature matrix-vector products for second-order gradient descent

We propose a generic method for iteratively approximating various second-order gradient steps - Newton, Gauss-Newton, Levenberg-Marquardt, and natural gradient - in linear time per iteration, using special curvature matrix-vector products that can be computed in O(n). Two recent acceleration techniq...

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Bibliographic Details

Published in:	Neural computation Vol. 14; no. 7; p. 1723
Main Author:	Schraudolph, Nicol N
Format:	Journal Article
Language:	English
Published:	United States 01.07.2002
Subjects:	Acceleration Algorithms Neural Networks (Computer) Stochastic Processes
ISSN:	0899-7667
Online Access:	Get more information
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