On the convergence rate of kernel-based sequential greedy regression
Summary: 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 the complexity measure of hypothesis space with covering numbers. A careful analysis shows the error has a satisfactory decay rate under mild conditions.
scientific article; zbMATH DE number 6134129
scientific article