In this article, by combining with a screening rule, we show how to eliminate useless features of the iterates generated by online algorithms, and thereby ...
A sequential screening strategy. We can directly apply Corollary 3.1 to screen out variables while running SGD. However, the effectiveness of this rule will ...
In this paper, by combining with a screening rule, we show how to eliminate useless features of the iterates generated by online algorithms, and thereby enforce ...
In this article, by combining with a screening rule, we show how to eliminate useless features of the iterates generated by online algorithms, and thereby ...
In this article, by combining with a screening rule, we show how to eliminate useless features of the iterates generated by online algorithms, and thereby ...
A novel accelerated doubly stochastic gradient descent method for sparsity regularized loss minimization problems, which can reduce the number of block ...
Fingerprint. Dive into the research topics of 'Variable screening for sparse online regression'. Together they form a unique fingerprint.
It combines the advantages of lasso and ridge regression. It is a method to solve the group variable selection with unknown variable grouping. Compared with the ...
There are several approaches to generalize variable selection from linear to non-linear ... Under the linear regression model, Fan and Lv (2008) proposed ...
Nov 2, 2022 · This paper reports on our study of a sparse neural network regression method based on a single hidden layer architecture and ...
Missing: Screening | Show results with:Screening