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Abstract: In this paper we prove sanity-check bounds for the error of the leave-one-out crossvalidation estimate of the generalization error: that is, bounds showing that the worst-case error of this estimate is not much worse than that of the training error estimate. The name sanity-check refers to the fact that although we often expect the leave-one-out estimate to perform considerably better than the training error estimate, we are here only seeking assurance that its performance will not be... (Update)
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BibTeX entry: (Update)
Kearns, M. J. and Ron, D. (1997). Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. In Proceedings of the Tenth Annual Conference on Computational Learning Theory. Morgan Kaufmann. http://citeseer.ist.psu.edu/kearns97algorithmic.html More
@inproceedings{ kearns97algorithmic,
author = "Michael J. Kearns and Dana Ron",
title = "Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation",
booktitle = "Computational Learing Theory",
pages = "152-162",
year = "1997",
url = "citeseer.ist.psu.edu/kearns97algorithmic.html" }
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