Online Learning Based on Online DCA and Application to Online Classification

被引：8

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

页码：759 / 793

页数：35

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