On the Convergence Rate of Kernel-Based Sequential Greedy Regression

الملخص EN

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.