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Abstract: In model selection procedures in supervised learning, a model is usually chosen so that the expected test error over all possible test input points is minimized. On the other hand, when the test input points (without output values) are available in advance, it is more effetive to choose a model so that the test error only at the test input points at hand is minimized. In this paper, we follow this idea and derive an estimator of the test error at the given test input points for linear... (Update)
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BibTeX entry: (Update)
@misc{ sugiyama-estimating,
author = "Masashi Sugiyama",
title = "Estimating the Error at Given Test Input Points for Linear Regression",
url = "citeseer.ist.psu.edu/715691.html" }
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