Abstract
Model selection is linked to model assessment, which is the problem of
comparing different models, or model parameters, for a specific learning
task. For supervised learning, the standard practical technique is crossvalidation,
which is not applicable for semi-supervised and unsupervised
settings. In this paper, a new model assessment scheme is introduced
which is based on a notion of stability. The stability measure yields an
upper bound to cross-validation in the supervised case, but extends to
semi-supervised and unsupervised problems. In the experimental part,
the performance of the stability measure is studied for model order selection
in comparison to standard techniques in this area.
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