Transductive learning is a generalization of semi-supervised learning which attempts to learn a distinctive classifier from large amounts of unlabeled data. In addition, Universum data can bring prior knowledge to the classifier. The Universum data mean the data which do not belong to the positive or negative classes, which can improve the performance of the learning task. In this paper, we address the problem of transductive learning with Universum data, and propose a new method, called information entropy-based transductive support vector machine with Universum data(IEB-TUSVM), which mainly consists of two steps. In the first Nstep, we propose an information entropy-based method to select the informative examples from the source Universum data to obtain the informative Universum data. In the second step, we take the selected Universum data into the semi-supervised classification, which is further solved by the Lagrange method. We further analyze the computational complexity of the proposed method. Extensive experiments have shown that IEB-TUSVM method outperforms state-of-the-art semi-supervised methods and the Universum learning methods.

转换学习是半监督学习的一种概括，它试图从大量未标记的数据中学习独特的分类器。另外，Universum数据可以将先验知识带到分类器。Universum数据是指不属于肯定或否定类别的数据，可以提高学习任务的性能。本文针对Universum数据进行跨语言学习的问题，提出了一种基于Universum数据的基于信息熵的跨语言支持向量机（IEB-TUSVM），该方法主要包括两个步骤。在第一个Nstep中，我们提出一种基于信息熵的方法，从源Universum数据中选择信息量示例，以获得信息量Universum数据。第二步 我们将选定的Universum数据纳入半监督分类中，这可以通过Lagrange方法进一步解决。我们进一步分析了该方法的计算复杂度。广泛的实验表明，IEB-TUSVM方法优于最新的半监督方法和Universum学习方法。