Kernel carpentry for online regression using randomly varying coefficient model

N.U. Edakunni, S. Schaal, S. Vijayakumar

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

Abstract

We present a Bayesian formulation of locally weighted learning (LWL) using the novel concept of a randomly varying coefficient model. Based on this, we propose a mechanism for multivariate non-linear regression using spatially localised linear models that learns completely independent of each other, uses only local information and adapts the local model complexity in a data driven fashion. We derive online updates for the model parameters based on variational Bayesian EM. The evaluation of the proposed algorithm against other state-of-the-art methods reveal the excellent, robust generalization performance beside surprisingly efficient time and space complexity properties. This paper, for the first time, brings together the computational efficiency and the adaptability of 'non-competitive' locally weighted learning schemes and the modelling guarantees of the Bayesian formulation.
Original languageEnglish
Title of host publicationProc. International Joint Conference on Artificial Intelligence (IJCAI '07), Hyderabad, India
PublisherMorgan Kaufmann
Pages762-767
Number of pages6
Publication statusPublished - 2007

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