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Gibbsboost: a Boosting Algorithm using a Sequential Monte Carlo Approach

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4 Author(s)
Nakada, Y. ; Grad. Sch. of Sci. & Eng., Waseda Univ., Tokyo ; Mouri, Y. ; Hongo, Y. ; Matsumoto, T.

This study proposes a novel boosting algorithm, GibbsBoost. A Gibbs distribution of a weaklearner sequence with a specific loss (energy) function is used in this algorithm as the posterior distribution in Bayesian learning. Weaklearner sequence samples are recursively drawn from the distribution via sequential Monte Carlo. The predictions are derived from a combination of the weaklearner sequence samples. The proposed algorithm is demonstrated by using a numerical example.

Published in:

Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on

Date of Conference:

6-8 Sept. 2006