Online Bayesian learning for dynamical classification problem using natural sequential prior
Kazue Sega, Yohei Nakada, Takashi Matsumoto · 2008
Classification problems in dynamical environments are in many fields,including signal processing and pattern recognition. In this paper, we propose a novel Bayesian approach to classification in a dynamical environment. The proposed approach employs natural sequential prior to improve online learning for an online classifier model. By using the natural sequential prior,the proposed approach describes the dynamical changes in the classifier modelpsilas parameters in a more natural manner. For comparison,the proposed approach and a conventional approach are validated by means of several numerical experiments.