摘要
生成式模型需要对复杂的联合概率密度建模,并估计较多的参数,为此,文中提出了一种基于最小熵正则化的半监督分类算法.该算法利用Havrda-Charvat's结构α-熵作为目标的正则项,并用拟牛顿法进行求解.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标记.在UCI数据库上的仿真实验结果表明,所提出的算法即使在有标记数据较少的情况下仍能获得较低的分类误差.
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.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2010年第1期87-91,共5页
Journal of South China University of Technology(Natural Science Edition)
基金
广东省-教育部产学研结合项目(2007B090400031)
广东省科技计划项目(2008B080701005)
