期刊
MACHINE LEARNING
卷 48, 期 1-3, 页码 9-23出版社
KLUWER ACADEMIC PUBL
DOI: 10.1023/A:1013943418833
关键词
model selection; parametric regression; uniform convergence bounds
Model selection is an important ingredient of many machine learning algorithms, in particular when the sample size in small, in order to strike the right trade-off between overfitting and underfitting. Previous classical results for linear regression are based on an asymptotic analysis. We present a new penalization method for performing model selection for regression that is appropriate even for small samples. Our penalization is based on an accurate estimator of the ratio of the expected training error and the expected generalization error, in terms of the expected eigenvalues of the input covariance matrix.
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