Title :
Least Square Regressions with Coefficient Regularization
Author :
Peixin, Ye ; Baohuai, Sheng
Author_Institution :
Sch. of Math., Nankai Univ., Tianjin, China
Abstract :
We consider the least square regression with data dependent hypothesis and coefficient regularization 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.
Keywords :
learning (artificial intelligence); least squares approximations; regression analysis; approximation error; coefficient regularization; data dependent hypothesis; least square regression; Approximation error; Computational modeling; Distributed databases; Hilbert space; Kernel; Support vector machines; System-on-a-chip; Coefficient Regularization; Data Dependent Hypothesis; General Kernel; Least Square Regressions;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on
Conference_Location :
Shenzhen, Guangdong
Print_ISBN :
978-1-61284-289-9
DOI :
10.1109/ICICTA.2011.50
