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On the Convergence of Leveraging

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84153

Rätsch,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Rätsch, G., Mika, S., & Warmuth, M. (2002). On the Convergence of Leveraging. Advances in Neural Information Processing Systems, 487-494.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-DF0F-0
Zusammenfassung
We give an unified convergence analysis of ensemble learning methods including e.g. AdaBoost, Logistic Regression and the Least-Square-Boost algorithm for regression. These methods have in common that they iteratively call a base learning algorithm which returns hypotheses that are then linearly combined. We show that these methods are related to the Gauss-Southwell method known from numerical optimization and state non-asymptotical convergence results for all these methods. Our analysis includes ℓ1-norm regularized cost functions leading to a clean and general way to regularize ensemble learning.