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Metropolis-Hastings sampling in a FilterBoost music classifier

Balázs Kégl 1, 2, 3 Thierry Bertin-Mahieux Douglas Eck
2 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Rejection sampling is a useful technique for performing supervised learning on training sets too large to be learned in their entirety. FilterBoost is a recent extension to AdaBoost which uses rejection sampling in an online learning framework and has been shown to work for automatic tagging of music. In this paper we improve on FilterBoost by adding Metropolis-Hastings sampling, thus allowing the algorithm to focus on hard-to-classify examples. We describe how our knowledge of artist-level similarity can be used effectively in a Metropolis-Hastings framework and demonstrate a significant increase in classification accuracy over standard FilterBoost.
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https://hal.inria.fr/inria-00428923
Contributor : Balázs Kégl <>
Submitted on : Thursday, October 29, 2009 - 11:40:11 PM
Last modification on : Thursday, July 8, 2021 - 3:49:23 AM

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  • HAL Id : inria-00428923, version 1

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Balázs Kégl, Thierry Bertin-Mahieux, Douglas Eck. Metropolis-Hastings sampling in a FilterBoost music classifier. International Workshop on Machine Learning and Music (ICML08 Workshop), Jul 2008, Helsinki, Finland. ⟨inria-00428923⟩

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