Concentration estimates for learning with unbounded sampling
Kernel based least square regression learning with unbounded sampling processes is studied. By a moment hypothesis for unbounded sampling, an approximation assumption for the regression function, and capacity condition for hypothesis space, sharper learning rates are derived. In the error analysis, a probability inequality for unbounded random variables is introduced, and an iteration technique is used.
regularization in reproducing kernel Hilbert spaces
scientific article; zbMATH DE number 6153873
scientific article