- 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.
- Copyright:
- 2006 by The University of Edinburgh. All Rights Reserved
- Links To Paper
- No links available
- Bibtex format
- @InProceedings{EDI-INF-RR-0883,
- author = {
Narayanan Edakkunni
and Stefan Schaal
and Sethu Vijayakumar
},
- title = {Kernel Carpentry for Online Regression using Randomly Varying Coefficient Model},
- book title = {Proceedings of the International Joint Conference on Artificial Intelligence 2007},
- year = 2006,
- }
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