Early stopping for kernel boosting algorithms: A general analysis with localized complexities

Early stopping of iterative algorithms is a widely-used form of regularization in statistics, commonly used in conjunction with boosting and related gradienttype algorithms. Although consistency results have been established in some settings, such estimators are less well-understood than their analogues based on penalized regularization.