Learning rates of least-square regularized regression with strongly mixing observation

被引：4

作者：

Zhang, Yongquan ^{[1
]}

Cao, Feilong ^{[1
]}

Yan, Canwei ^{[1
]}

机构：

[1] China Jiliang Univ, Dept Informat & Math Sci, Hangzhou 310018, Zhejiang, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2012年 / 3卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Sample error; Regularization error; Exponentially strongly mixing; Polynomial kernels; Jackson interpolation operator;

D O I：

10.1007/s13042-011-0058-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper considers the regularized learning algorithm associated with the least-square loss, strongly mixing observations and reproducing kernel Hilbert spaces. We first give the bound of the sample error with exponentially strongly mixing observations and the rate of approximation by Jackson-type theorem of approximation based on exponentially strongly mixing sequence. Then the generalization error of the least-square regularized regression is obtained by estimating sample error and regularization error.

引用

页码：277 / 283

页数：7

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