Stability and Generalization

Bousquet, O; Elisseeff, A

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Stability and Generalization

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http://www.jmlr.org/papers/volume2/bousquet02a/bousquet02a.pdf
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Citation

Bousquet, O., & Elisseeff, A. (2002). Stability and Generalization. The Journal of Machine Learning Research, 2, 499-526.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E0C2-2

Abstract

We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds
based on the empirical error and the leave-one-out error. The
methods we use can be applied in the regression framework as well
as in the classification one when the classifier is obtained by
thresholding a real-valued function. We study the stability
properties of large classes of learning algorithms such as
regularization based algorithms. In particular we focus on Hilbert
space regularization and Kullback-Leibler regularization. We
demonstrate how to apply the results to SVM for regression and
classification.