On the Convergence Rate of Kernel-Based Sequential Greedy Regression - Xiaoyin Wang; Xiaoyan Wei; Zhibin Pan (Abstract and Applied Analysis)
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.