Transductive gaussian process regression with automatic model selection

Authors:
Quoc V. Le;Alex J. Smola;Thomas Gä/rtner;Yasemin Altun
Affiliations:
RSISE, Australian National University/ Statistical Machine Learning Program, National ICT Australia, ACT, Australia;RSISE, Australian National University/ Statistical Machine Learning Program, National ICT Australia, ACT, Australia;Fraunhofer IAIS, Sankt Augustin, Germany;Toyota Technological Institute at Chicago, Chicago, IL
Venue:
ECML'06 Proceedings of the 17th European conference on Machine Learning
Year:
2006

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Abstract

In 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.