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Learning a compact predictive model in an online setting has recently gained a great deal of attention. The combination of online learning with sparsity-inducing regularization enables faster learning with a smaller memory space than the previous learning frameworks. Many optimization methods and learning algorithms have been developed on the basis of online learning with L1-regularization. L1-regularization...
We propose a new truncation framework for online supervised learning. Learning a compact predictive model in an online setting has recently attracted a great deal of attention. The combination of online learning with sparsity-inducing regularization enables faster learning with a smaller memory space than a conventional learning framework. However, a simple combination of these triggers the truncation...
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