Stability-Based Model Selection

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Tilman Lange, Mikio Braun, Volker Roth, Joachim Buhmann


Model selection is linked to model assessment, which is the problem of comparing different models, or model parameters, for a speciļ¬c learning task. For supervised learning, the standard practical technique is cross- validation, 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 se- lection in comparison to standard techniques in this area.