Abstract：A Logistic L1/2 regularization model with its efficient solution algorithm is proposed. By the proposed model, which is constructed on the basis of the L1/2 regularization theory, the variable selection capability is enhanced and the over-fitting problem of the traditional model is alleviated. The proposed algorithm with high computational efficiency is designed by the coordinate descent technique. The experimental results on synthetic and real datasets indicate that the proposed method outperforms the traditional Logistic regression and the L1 regularized Logistic regression on both variable selection and tendency prediction.
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