Learning rates for multi-kernel linear programming classifiers

被引：0
|
作者：
Cao, Feilong [1 ]
Xing, Xing [1 ]
机构：
[1] China Jiliang Univ, Dept Math, Hangzhou 310018, Zhejiang, Peoples R China
来源：
FRONTIERS OF MATHEMATICS IN CHINA | 2011年 / 6卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Multi-kernel; linear programming; learning rate; classification; SOFT MARGIN CLASSIFIERS; CLASSIFICATION; CONSISTENCY;
D O I：
10.1007/s11464-011-0103-3
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
In this paper, we consider the learning rates of multi-kernel linear programming classifiers. Our analysis shows that the convergence behavior of multi-kernel linear programming classifiers is almost the same as that of multi-kernel quadratic programming. This is implemented by setting a stepping stone between the linear programming and the quadratic programming. An upper bound is presented for general probability distributions and distribution satisfying some Tsybakov noise condition.
引用
收藏
页码：203 / 219
页数：17
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