Infinite Mixtures of Gaussian Process Experts
2002
Conference Paper
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We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Using a input-dependent adaptation of the Dirichlet Process, we implement a gating network for an infinite number of Experts. Inference in this model may be done efficiently using a Markov Chain relying on Gibbs sampling. The model allows the effective covariance function to vary with the inputs, and may handle large datasets -- thus potentially overcoming two of the biggest hurdles with GP models. Simulations show the viability of this approach.
Author(s): | Rasmussen, CE. and Ghahramani, Z. |
Year: | 2002 |
Day: | 0 |
Editors: | Dietterich, Thomas G.; Becker, Suzanna; Ghahramani, Zoubin |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Digital: | 0 |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inproceedings{2297, title = {Infinite Mixtures of Gaussian Process Experts}, author = {Rasmussen, CE. and Ghahramani, Z.}, editors = {Dietterich, Thomas G.; Becker, Suzanna; Ghahramani, Zoubin}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, year = {2002}, doi = {} } |