Boosting on Manifolds: Adaptive Regularization of Base Classifiers
Ligen Wang, Balázs Kégl · 2004
In this paper we propose to combine two powerful ideas, boosting and manifold learning. On the one hand, we improve ADABOOST by incor-porating knowledge on the structure of the data into base classifier design and selection. On the other hand, we use ADABOOST’s efficient learn-ing mechanism to significantly improve supervised and semi-supervised algorithms proposed in the context of manifold learning. Beside the spe-cific manifold-based penalization, the resulting algorithm also accommo-dates the boosting of a large family of regularized learning algorithms. 1