关键词: 机器学习, 多示例多标记, 支持向量机（SVM）, 半监督学习

Abstract: Multi-instance multi-label learning is a new machine learning framework. In MIML framework, an example is described by multiple instances and associated with multiple class labels. Algorithm MIMLSVM+ decomposes the MIML problem into multiple independent binary classification problems. However, the degeneration process may lose information, which will influence the classification performance. Algorithm E-MIMLSVM+ by using multitask learning techniques is utilized to incorporate label correlations to improve the algorithm MIMLSVM+. In order to make full use of the unlabeled samples to improve the classification accuracy, this paper improves the E-MIMLSVM+ algorithm by using the semi-supervised support vector machine TSVM. In this paper, the algorithm is compared with other MIML algorithms. The experimental results show that the algorithm has achieved good classification results.

Key words: machine learning, Multi-Instance Multi-Label（MIML）, Support Vector Machine（SVM）, semi-supervised learning