Transductive Gaussian process regression with automatic model selection

Date

2006

Authors

Le, Quoc
Smola, Alexander
Gaertner, Thomas
Altun, Yasemin

Journal Title

Journal ISSN

Volume Title

Publisher

Springer

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.

Description

Keywords

Keywords: Automatic model selection; Gaussian process regression; Moment matching; Approximation theory; Information theory; Learning algorithms; Mathematical models; Regression analysis; Learning systems

Citation

Source

Machine Learning: Proceedings of ECML 2006

Type

Conference paper

Book Title

Entity type

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DOI

Restricted until

2037-12-31