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Abstract: Non-quadratic regularizers, in particular the ` 1 norm regularizer can yield sparse solutions that generalize well. In this work we propose the Generalized Subspace Information Criterion (GSIC) that allows to predict the generalization error for this useful family of regularizers. We show that under some technical assumptions GSIC is an asymptotically unbiased estimator of the generalization error. GSIC is demonstrated to have a good performance in experiments with the ` 1 norm regularizer ... (Update)
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BibTeX entry: (Update)
@misc{ tsuda-subspace,
author = "Koji Tsuda and Masashi Sugiyama and Klaus-Robert Müller",
title = "Subspace Information Criterion for Sparse Regressors",
url = "citeseer.ist.psu.edu/tsuda01subspace.html" }
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