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Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees.
A stochastic approximation method
H. Robbins and S. Monro · 1951
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On the convergence of coordinate descent method for convex differentiable minimization
Z.Q. Luo and P. Tseng · 1992
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Making large-scale support vector machine learning practical
T. Joachims · 1998
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A statistical study of on-line learning
N. Murata · 1998
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Fast training of Support Vector Machines using sequential minimal optimization
J. C. Platt · 1998
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Successive overrelaxation for support vector machines
O. Mangasarian and D. Musicant · 1999
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Large scale online learning
L.B.Y. Le Cun · 2004
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Solving large scale linear prediction problems using stochastic gradient descent algorithms
T. Zhang · 2004
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QP algorithms with guaranteed accuracy and run time for support vector machines
D. Hush, P. Kelly, C. Scovel, and I. Steinwart · 2006
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Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
S. Shalev-Shwartz, Y. Singer, and N. Srebro · 2007
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The tradeoffs of large scale learning
L. Bottou and O. Bousquet · 2008
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Exponentiated gradient algorithms for conditional random fields and max-margin markov networks
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A dual coordinate descent method for large-scale linear SVM
SVM optimization: Inverse dependence on training set size
S. Shalev-Shwartz and N. Srebro · 2008
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Stochastic methods for l 1 l_{1} regularized loss minimization
Shai Shalev-Shwartz and Ambuj Tewari · 2009
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Fast rates for regularized objectives
K. Sridharan, N. Srebro, and S. Shalev-Shwartz · 2009
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Stochastic block-coordinate frank-wolfe optimization for structural svms
S. Lacoste-Julien, M. Jaggi, M. Schmidt, and P. Pletscher · 2012
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Nicolas Le Roux, Mark Schmidt, and Francis Bach · 2012
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C.J. Hsieh, K.W. Chang, C.J. Lin, S.S. Keerthi, and S. Sundararajan · 2008
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Efficiency of coordinate descent methods on huge-scale optimization problems
Y. Nesterov · 2012
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