Kernel Carpentry for Online Regression using Randomly Varying Coefficient Model
Narayanan Edakunni, Stefan Schaal and Sethu Vijayakumar
In: 20th International Joint Conference on Artificial Intelligence (IJCAI '07), 6-12 Jan 2007, Hyderabad, India.
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.