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NIPS
2004

Boosting on Manifolds: Adaptive Regularization of Base Classifiers

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Boosting on Manifolds: Adaptive Regularization of Base Classifiers
In this paper we propose to combine two powerful ideas, boosting and manifold learning. On the one hand, we improve ADABOOST by incorporating knowledge on the structure of the data into base classifier design and selection. On the other hand, we use ADABOOST's efficient learning mechanism to significantly improve supervised and semi-supervised algorithms proposed in the context of manifold learning. Beside the specific manifold-based penalization, the resulting algorithm also accommodates the boosting of a large family of regularized learning algorithms.
Balázs Kégl, Ligen Wang
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NIPS
Authors Balázs Kégl, Ligen Wang
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