Recursive Bayesian regression modeling and learning

Jen-Tzung Chien*, Jung Chun Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This paper presents a new Bayesian regression and learning algorithm for adaptive pattern classification. Our aim is to continuously update regression parameters to meet nonstationary environments for real-world applications. Here, a kernel regression model is used to represent twoclass data. The initial regression parameters are estimated by maximizing the likelihood of training data. To activate online learning, we properly express the randomness of regression parameters as a conjugate prior, which is a normal-gamma distribution. When new adaptation data are enrolled, we can accumulate sufficient statistics and come up with a new normal-gamma distribution as the posterior distribution. We therefore exploit a recursive Bayesian algorithm for online regression and learning. Regression parameters are incrementally adapted to the newest environments. Robustness of classification rule is assured using online regression parameters. In the experiments on face recognition, the proposed regression algorithm outperforms support vector machine and relevance vector machine for different numbers of adaptation data.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
DOIs
StatePublished - 6 Aug 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: 15 Apr 200720 Apr 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
ISSN (Print)1520-6149

Conference

Conference2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period15/04/0720/04/07

Keywords

  • Incremental adaptation
  • Kernel regression
  • Pattern recognition
  • Recursive Bayesian learning
  • Support vector machine

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  • Cite this

    Chien, J-T., & Chen, J. C. (2007). Recursive Bayesian regression modeling and learning. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 [4217469] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2). https://doi.org/10.1109/ICASSP.2007.366296