lecture19 (1)

lecture19 (1) - Suggested Reading • V N Vapnik...

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Lecture 19: Leave-one-out approximations Sayan Mukherjee Description We introduce the idea of cross-validation, leave-one-out in its extreme form. We show that the leave-one-out estimate is almost unbiased. We then show a series of approximations and bounds on the leave-one-out error that are used for computational efficiency. First this is shown for least-squares loss then for the SVM loss function. We close by reporting in a worst case analysis the leave-one-out error is not a significantly better estimate of expected error than is the training error.
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Unformatted text preview: Suggested Reading • V. N. Vapnik. Statistical Learning Theory. Wiley, 1998. • Chapelle et al Choosing Multiple Parameters for Support Vector Machines. Machine Learning, 2002. • Wahba, G. Spline Models for Observational Data Series in Applied Mathematics, Vol. 59, SIAM, 1990. • T. Jaakkola and D. Haussler. Probabilistic kernel regression models. In Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999....
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