Zobrazeno 1 - 4
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pro vyhledávání: '"Shibagaki, Atsushi"'
Autor:
Shibagaki, Atsushi, Takeuchi, Ichiro
We study primal-dual type stochastic optimization algorithms with non-uniform sampling. Our main theoretical contribution in this paper is to present a convergence analysis of Stochastic Primal Dual Coordinate (SPDC) Method with arbitrary sampling. B
Externí odkaz:
http://arxiv.org/abs/1703.07056
We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier. When the entire dataset is large, even if t
Externí odkaz:
http://arxiv.org/abs/1606.00136
The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduced safe screening allows us to identify a part of non-active features/s
Externí odkaz:
http://arxiv.org/abs/1602.02485
Careful tuning of a regularization parameter is indispensable in many machine learning tasks because it has a significant impact on generalization performances. Nevertheless, current practice of regularization parameter tuning is more of an art than
Externí odkaz:
http://arxiv.org/abs/1502.02344