PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

When training and test sets are different: characterising learning transfer
Amos Storkey
In: Dataset Shift in Machine Learning (2009) MIT Press , Cambridge, Massachusetts , pp. 1-28. ISBN 978-0-262-17005-5

Abstract

A number of common forms of dataset shift are introduced, and each is related to a particular form of causal probabilistic model. Examples are given for the different types of shift, and some corresponding modelling approaches. By characterising dataset shift in this way, there is potential for the development of models which capture the specific types of variations, combine different modes of variation, or do model selection to assess whether dataset shift is an issue in particular circumstances. As an example of how such models can be developed, an illustration is provided for one approach to adapting Gaussian process methods for a particular type of dataset shift called Mixture Component Shift.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4290
Deposited By:Amos Storkey
Deposited On:09 March 2009