Incremental Gaussian Processes |
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| Abstract | In this paper, we consider Tipping's relevance vector machine (RVM) and formalize an incremental training strategy as a variant of the expectation-maximization (EM) algorithm that we call subspace EM. Working with a subset of active basis functions, the sparsity of the RVM solution will ensure that the number of basis functions and thereby the computational complexity is kept low. We also introduce a mean field approach to the intractable classification
model that is expected to give a very good approximation to exact Bayesian inference and contains the Laplace approximation as a special case. We test the algorithms on two large data sets with 10^3-10^4 examples. The results indicate that Bayesian learning of large data sets, e.g. the MNIST database is realistic. |
| Keywords | Gaussian Processes, Incremental Methods, Bayesian Kernel Methods, Mean Field Classification, Computational Complexity |
| Type | Conference paper [With referee] |
| Conference | Advances in Neural Processing Systems |
| Year | 2002 |
| Electronic version(s) | [ps] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |