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Generalized Stochastic Gradient Learning

By George W. Evans, Seppo Honkapohja and Noah Williams

Abstract

We study the properties of generalized stochastic gradient (GSG) learning in forwardlooking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both di1er from and are related to E-stability, which governs stability under least squares learning. SG algorithms are sensitive to units of measurement and we show that there is a transformation of variables for which E-stability governs SG stability. GSG algorithms with constant gain have a deeper justification in terms of parameter drift, robustness and risk sensitivity

Topics: E-stability, recursive least squares, robust estimation, Classification-JEL: C62, C65, D83, E10, E17, Adaptive Learning
Publisher: Faculty of Economics
Year: 2006
OAI identifier: oai:www.repository.cam.ac.uk:1810/131589
Provided by: Apollo

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