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Stochastic gradient descent algorithms for training linear and kernel predictors are gaining more and more importance, thanks to their scalability.
A stochastic approximation method
H. Robbins and S. Monro · 1951
Earlier work this paper cites.
The Perceptron: A probabilistic model for information storage and organization in the brain
F. Rosenblatt · 1958
Earlier work this paper cites.
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C.-C. Chang and C.-J. Lin · 2001
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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