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For large scale learning problems, it is desirable if we can obtain the optimal model parameters by going through the data in only one pass.
On asymptotic normality in stochastic approximation
Václav Fabian · 1968
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Asymptotically efficient stochastic approximation; the RM case
VÁclav Fabian · 1973
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Acceleration of stochastic approximation by averaging
Boris T. Polyak and Anatoli. B. Juditsky · 1992
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Gradient-based learning applied to document recognition
Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Shun-ichi Amari, Hyeyoung Park, and Kenji Fukumizu · 2000
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RCV1: A new benchmark collection for text categorization research
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Soeren Sonnenburg, Vojtech Franc, Elad Yom-Tov, and Michele Sebag · 2008
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Robust stochastic approximation approach to stochastic programming
Arkadi Nemirovski, Anatoli Juditski, Guanghui Lan, and Alexander Shapiro · 2009
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Stochastic methods for ℓ 1 \ell_{1} regularized loss minimization
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LIBLINEAR: A library for large linear classification
Rong-En Fan, Kai-Wei Change, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin · 2008
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