Boosting methods while being among the best classification methods developed so far, are known to degrade performance in case of noisy data and overlapping classes. In this paper we propose a new upper generalization bound for weighted averages of hypotheses, which uses posterior estimates for training objects and is based on reduction of binary classification problem with overlapping classes to a deterministic problem. If we are given accurate posterior estimates, proposed bound is lower than existing bound by Schapire et al [25]. We design an AdaBoost-like algorithm which optimizes proposed generalization bound and show that incorporated with good posterior estimates it performs better than the standard AdaBoost on real-world data sets. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Barinova, O., & Vetrov, D. (2009). ODDboost: Incorporating posterior estimates into adaboost. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5632 LNAI, pp. 178–190). https://doi.org/10.1007/978-3-642-03070-3_14
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