An experimental and theoretical comparison of model selection methods. Machine Learning 27

An experimental and theoretical comparison of model selection methods. Machine Learning 27 (1997)

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by Michael Kearns , Yishay Mansour Y , Andrew Y. Ng , Dana Ron Z

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@MISC{Kearns97anexperimental,
    author = {Michael Kearns and Yishay Mansour Y and Andrew Y. Ng and Dana Ron Z},
    title = {An experimental and theoretical comparison of model selection methods. Machine Learning 27},
    year = {1997}
}

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Abstract

In the model selection problem, we must balance the complexity of a statistical model with its goodness of fit to the training data. This problem arises repeatedly in statistical estimation, machine learning, and scientific inquiry in general.

Citations

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