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Generalized Stochastic Gradient Learning / George W. Evans, Seppo Honkapohja, Noah Williams.
- Format:
- Book
- Author/Creator:
- Evans, George W.
- Series:
- Technical Working Paper Series (National Bureau of Economic Research) no. t0317.
- NBER technical working paper series no. t0317
- Language:
- English
- Physical Description:
- 1 online resource: illustrations (black and white);
- Place of Publication:
- Cambridge, Mass. National Bureau of Economic Research 2005.
- Summary:
- We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ 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.
- Notes:
- Print version record
- October 2005.
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