l1-Norm support vector machine for ranking with exponentially strongly mixing sequence

被引：2

作者：

Chen, Di-Rong ^{[1
]}

Huang, Shou-You ^{[1
]}

机构：

[1] Beijing Univ Aeronaut & Astronaut, Dept Math, Beijing 100191, Peoples R China

来源：

INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING | 2014年 / 12卷 / 05期

关键词：

U-statistic; support vector machine; exponentially strongly mixing sequence; excess risk; ranking rule; CONVERGENCE; CLASSIFIERS; RATES;

D O I：

10.1142/S0219691314610013

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The problem of ranking/ordering instances, instead of simply classifying them, has recently gained much attention in machine learning. Ranking from binary comparisons is a ubiquitous problem in modern machine learning applications. In this paper, we consider l(1)-norm SVM for ranking. As well known, learning with l(1)-norm restrictions usually leads to sparsity. Moreover, instead of independently draw sample sequence, we are given sample of exponentially strongly mixing sequence. Under some mild conditions, a learning rate is established.

引用

页数：13

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