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PRL
2007

Unifying multi-class AdaBoost algorithms with binary base learners under the margin framework

13 years 10 months ago
Unifying multi-class AdaBoost algorithms with binary base learners under the margin framework
Multi-class AdaBoost algorithms AdaBooost.MO, -ECC and -OC have received a great attention in the literature, but their relationships have not been fully examined to date. In this paper, we present a novel interpretation of the three algorithms, by showing that MO and ECC perform stage-wise functional gradient descent on a cost function defined over margin values, and that OC is a shrinkage version of ECC. This allows us to strictly explain the properties of ECC and OC, empirically observed in prior work. Also, the outlined interpretation leads us to introduce shrinkage as regularization in MO and ECC, and thus to derive two new algorithms: SMO and SECC. Experiments on diverse databases are performed. The results demonstrate the effectiveness of the proposed algorithms and validate our theoretical findings. Ó 2006 Elsevier B.V. All rights reserved.
Yijun Sun, Sinisa Todorovic, Jian Li
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where PRL
Authors Yijun Sun, Sinisa Todorovic, Jian Li
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