Uncertainty in Artificial Intelligence
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Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost
Ping Li
Abstract:
Logitboost is an influential boosting algorithm for classification. In this paper, we develop robust logitboost to provide an explicit formulation of tree-split criterion for building weak learners (regression trees) for logitboost. This formulation leads to a numerically stable implementation of logitboost. We then propose abc-logitboost for multi-class classification, by combining robust logitboost with the prior work of abc-boost. Previously, abc-boost was implemented as abc-mart using the mart algorithm. Our extensive experiments on multi-class classification compare four algorithms: mart, abcmart, (robust) logitboost, and abc-logitboost, and demonstrate the superiority of abc-logitboost. Comparisons with other learning methods including SVM and deep learning are also available through prior publications.
Keywords:
Pages: 302-311
PS Link:
PDF Link: /papers/10/p302-li.pdf
BibTex:
@INPROCEEDINGS{Li10,
AUTHOR = "Ping Li ",
TITLE = "Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "302--311"
}


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