Abstract and Applied Analysis
Volume 2012 (2012), Article ID 619138, 9 pages
Research Article

On the Convergence Rate of Kernel-Based Sequential Greedy Regression

1College of Sciences, Huazhong Agricultural University, Wuhan 430070, China
2Department of Statistics and Applied Mathematics, Hubei University of Economics, Wuhan 430205, China

Received 13 October 2012; Accepted 27 November 2012

Academic Editor: Jean M. Combes

Copyright © 2012 Xiaoyin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A kernel-based greedy algorithm is presented to realize efficient sparse learning with data-dependent basis functions. Upper bound of generalization error is obtained based on complexity measure of hypothesis space with covering numbers. A careful analysis shows the error has a satisfactory decay rate under mild conditions.