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Gibbsboost: a Boosting Algorithm using a Sequential Monte Carlo Approach

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4 Author(s)
Yohei Nakada ; Graduate School of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555 Japan, Tel/Fax: +81-3-5286-3377. E-mail: ; Yusuke Mouri ; Yasunori Hongo ; Takashi Matsumoto

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:

2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing

Date of Conference:

6-8 Sept. 2006