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Abstract: The informative vector machine (IVM) is a practical method for Gaussian process regression and classification. The IVM produces a sparse approximation to a Gaussian process by combining assumed density filtering with a heuristic for choosing points based on minimizing posterior entropy. This paper extends IVM in several ways. First, we propose a novel noise model that allows the IVM to be applied to a mixture of labeled and unlabeled data. Second, we use IVM on a blockdiagonal covariance ... (Update)
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BibTeX entry: (Update)
@misc{ lawrence-extensions,
author = "Neil D. Lawrence and John C. Platt and Michael I. Jordan",
title = "Extensions of the Informative Vector Machine",
url = "citeseer.ist.psu.edu/756331.html" }
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