Stochastic Dual Coordinate Ascent and its Proximal Extension for Regularized Loss Minimization
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Stochastic Dual Coordinate Ascent and its Proximal Extension for Regularized Loss Minimization
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. While the closely related Dual Coordinate Ascent (DCA) method has been implemented in various software packages, it has so far lacked good convergence analysis ...
Jan 2, 2013
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