首先分析了条件 Havrda-Charvat’s structural α-熵为什么是一个好的聚类标准，然后基于认识：一个好的聚类标准同时也是对无标记数据的一种好的刻画，提出了基于最小熵正则化的半监督分类模型，并用拟牛顿法对模型进行了求解。该算法既是判别式的，又是直推式的，从而降低了对模型的依赖程度，同时可以方便地预测训练集之外的示例的标记。在UCI数据库上的测试结果验证了该算法的有效性。

As the generative model needs modelling complex joint probability density and evaluating many parameters, a discriminant semi-supervised classification algorithm based on the regularization of minimum entropy is proposed. This algorithm uses Havrda-Charvat＇s structural α-entropy as the regularization item of the objective and employs the quasi-Newton method to solve the objective, which makes the algorithm discriminative and inductive and reduces the dependence of the algorithm on the model. At the same time, the algorithm can predict the labels of the out-of-sample data points easily. Simulated results of several UCI datasets demonstrate that the proposed algorithm is of low classification error even with few labeled data.