M. Korzen

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A generalization of the commonly used Maximum Likelihood based learning algorithm for the logistic regression model is considered. It is well known that using the Laplace prior (L 1 penalty) on model coefficients leads to a variable selection effect, when most of the coefficients vanish. It is argued that variable selection is not always desirable; it is(More)
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