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

页码：203 / 219

页数：17

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