基于相似度学习的多源迁移算法

针对与测试数据分布相同的训练数据不足,相关领域中存在大量的、与测试数据分布相近的训练数据的场景,提出一种基于相似度学习的多源迁移学习算法(SL-MSTL).该算法在经典SVM分类模型的基础上提出一种新的迁移分类模型,增加对多源域与目标域之间的相似度学习,可以有效地利用各源域中的有用信息,提高目标域的分类效果.实验的结果表明了SL-MSTL 算法的有效性和实用性.

Similarity-learning based multi-source transfer learning algorithm

For the proplem that the training data which have the same distribution with the test data are insuficient, but a lot of training data which have the similar distribution with the test data exist in the related field, a similarity-learning based multi-source transfer learning(SL-MSTL) algorithm is proposed. A similarity-learning based classification model is proposed in contrast to the classical support vector machine(SVM) model. Compared to the SVM model, the proposed similarity-learning based model can make better use of the source information and improve the classification performance. Experimental results show the effectiveness and the practicality of the proposed algorithm.