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Dataset Shift in Machine Learning$
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Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence

Print publication date: 2008

Print ISBN-13: 9780262170055

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262170055.001.0001

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On Bayesian Transduction: Implications for the Covariate Shift Problem

On Bayesian Transduction: Implications for the Covariate Shift Problem

Chapter:
(p.65) 4 On Bayesian Transduction: Implications for the Covariate Shift Problem
Source:
Dataset Shift in Machine Learning
Author(s):

Kai Hansen Lars

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262170055.003.0004

This chapter analyzes Bayesian supervised learning with extensions to semisupervised learning, and learning with covariate or dataset shift. The main result is an expression for the generalization optimal Bayesian procedure. The resulting “Bayesian transduction” average is optimal for a realizable model. For semisupervised learning, this implies that all available data, including unlabeled data, should be used in the likelihood, hence in forming the parameter posterior. In the case of covariate or dataset shift, the situation is contingent on the parameterization.

Keywords:   Bayesian supervised learning, semisupervised learning, covariate shift, dataset shift

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