Least Square Regressions with Coefficient Regularizatioir

摘要：

We consider the least square regression with data dependent hypothesis and coefficient regnlarization algorithms based on general kernel. An explicit expression of the solution of this kernel scheme is derived. Then we provide a sample error with a decay of O(-1/√m) and estimate the approximation error in terms of some kind of K-functional.

关键词： Least Square Regressions Data Dependent Hypothesis Coefficient Regnlarization General Kernel

作者: Ye Peixin Sheng Baohuai

作者单位: School of Mathematics, Nankai University, Tianjin 300071, China Department of Mathematics, Shaoxing College of Arts and Sciences Shaoxing, Zhejiang 312000, China

会议类型: 国际会议

会议名称: 2011 Fourth International Conference on Intelligent Computation Technology and Automation(2011年第四届智能计算技术与自动化国际会议 ICICTA 2011)

会议地点: 深圳

会议语种:英文

页码: 167-170

在线出版日期: 2011-03-28（万方平台首次上网日期，不代表论文的发表时间）