Online Learning Based on Online DCA and Application to Online Classification

被引：7
|
作者：
Thi, Hoai An Le [1 ]
Ho, Vinh Thanh [1 ]
机构：
[1] Univ Lorraine, LGIPM, F-57000 Metz, France
来源：
NEURAL COMPUTATION | 2020年 / 32卷 / 04期
关键词：
RELATIVE LOSS BOUNDS; ALGORITHMS; GRADIENT;
D O I：
10.1162/neco_a_01266
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We investigate an approach based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) for online learning techniques. The prediction problem of an online learner can be formulated as a DC program for which online DCA is applied. We propose the two so-called complete/approximate versions of online DCA scheme and prove their logarithmic/sublinear regrets. Six online DCA-based algorithms are developed for online binary linear classification. Numerical experiments on a variety of benchmark classification data sets show the efficiency of our proposed algorithms in comparison with the state-of-the-art online classification algorithms.
引用
收藏
页码：759 / 793
页数：35
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