| 摘要 |
Described is a technology by which a learned mechanism is developed by solving a minimization problem by using regularized dual averaging methods to provide regularized stochastic learning and online optimization. An objective function sums a loss function of the learning task and a regularization term. The regularized dual averaging methods exploit the regularization structure in an online learning environment, in a manner that obtains desired regularization effects, e.g., sparsity under L1-regularization.
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