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2004
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A Consistency-Based Model Selection for One-Class Classification

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A Consistency-Based Model Selection for One-Class Classification
Model selection in unsupervised learning is a hard problem. In this paper a simple selection criterion for hyperparameters in one-class classifiers (OCCs) is proposed. It makes use of the particular structure of the one-class problem. The mean idea is that the complexity of the classifier is increased until the classifier becomes inconsistent on the target class. This defines the most complex classifier which can still reliably be trained on the data. Experiments indicated the usefulness of the approach.
David M. J. Tax, Klaus-Robert Müller
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2004
Where ICPR
Authors David M. J. Tax, Klaus-Robert Müller
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