中文摘要：

提出一种L1/2正则化Logistic回归模型，并针对此模型构造有效的求解算法．文中模型基于L1/2正则化理论建立，有效改善传统模型存在的变量选择与计算过拟合问题．文中算法基于“坐标下降”思想构造，快速有效．在一系列人工和实际数据集上的实验表明，文中算法在分类问题中具有良好的变量选择能力和预测能力，优于传统Logistic回归和L1正则化Logistic回归．

英文摘要：

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.