Regularized Learning with Lipschitz Loss and Sample Dependent Hypothesis Spaces

被引：0
|
作者：
Sheng Baohuai [1 ]
Ye Peixin [2 ]
机构：
[1] Shaoxing Coll Arts & Sci, Dept Math, Shaoxing 312000, Zhejiang, Peoples R China
[2] Nankai Univ, LPMC, Sch Math, Tianjin 300071, Peoples R China
来源：
2011 INTERNATIONAL CONFERENCE ON COMPUTER, ELECTRICAL, AND SYSTEMS SCIENCES, AND ENGINEERING (CESSE 2011) | 2011年
关键词：
Sample depending Hypothesis spaces; convex analysis; Lipchitz loss; SUPPORT VECTOR MACHINES; REGRESSION; KERNELS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We study the error bounds for norm square regularized regressions associated with Lipchitz loss and sample depending reproducing kernel spaces. By giving the unique solution with sub gradients of the loss functions, we estimate the learning rates with the regularization parameters lambda and the sample number m.
引用
收藏
页码：131 / 133
页数：3
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