适用于不确定类标签数据学习的迁移支持向量机

为了解决包含不确定信息的分类学习问题，提出一种新的适用于不确定类标签数据的迁移支持向量机。该方法基于结构风险最小化模型，同时将源领域中所学知识、领域间的共享数据、目标领域中已标定的和不确定的数据纳入学习框架中，进而实现了源领域和目标领域的知识迁移。在多种真实数据集上的实验结果表明了所提出方法的有效性。

Transfer support vector machine for learning from data with uncertain labels

To address the learning problems which include unlabeled samples, a novel transfer support vector machine for learning from data with uncertain labels(TSVM-UL) is proposed based on the structure risk minimization model. This method takes knowledge of source domain, the common data between different domains, labeled samples and probabilities of unlabeled samples of target domain into account, and knowledge transferring between the source domain and the target domain is realized. Experiment results on several real-world datasets show the effectiveness of the proposed method.