Abstract
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.
Citation
Xiaoyin Wang. Xiaoyan Wei. Zhibin Pan. "On the Convergence Rate of Kernel-Based Sequential Greedy Regression." Abstr. Appl. Anal. 2012 1 - 9, 2012. https://doi.org/10.1155/2012/619138
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