  Developing efficient kernel methods for regression is very popular in the
past decade. In this paper, utilizing boosting on kernel-based weaker learners,
we propose a novel kernel-based learning algorithm called kernel-based
re-scaled boosting with truncation, dubbed as KReBooT. The proposed KReBooT
benefits in controlling the structure of estimators and producing sparse
estimate, and is near overfitting resistant. We conduct both theoretical
analysis and numerical simulations to illustrate the power of KReBooT.
Theoretically, we prove that KReBooT can achieve the almost optimal numerical
convergence rate for nonlinear approximation. Furthermore, using the recently
developed integral operator approach and a variant of Talagrand's concentration
inequality, we provide fast learning rates for KReBooT, which is a new record
of boosting-type algorithms. Numerically, we carry out a series of simulations
to show the promising performance of KReBooT in terms of its good
generalization, near over-fitting resistance and structure constraints.
