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Conference Paper

Transductive Gaussian Process Regression with Automatic Model Selection

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84051

Le,  QV
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83782

Altun,  Y
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Le, Q., Smola AJ, Gärtner, T., & Altun, Y. (2006). Transductive Gaussian Process Regression with Automatic Model Selection. In 17th European Conference on Machine Learning (ECML 2006) (pp. 306-317). Berlin, Germany: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D059-2
Abstract
n contrast to the standard inductive inference setting of predictive machine learning, in real world learning problems often the test instances are already available at training time. Transductive inference tries to improve the predictive accuracy of learning algorithms by making use of the information contained in these test instances. Although this description of transductive inference applies to predictive learning problems in general, most transductive approaches consider the case of classification only. In this paper we introduce a transductive variant of Gaussian process regression with automatic model selection, based on approximate moment matching between training and test data. Empirical results show the feasibility and competitiveness of this approach.