A multi-instance multi-label improved algorithm based on semi-supervised learning - Li Cunhe (Dianzi Jishu Yingyong, 2019)
The multi-instance multi-label learning framework is a new machine learning framework for solving ambiguity problems. In the multi-instance multi-label learning framework, an object is represented by a set of examples and is associated with a set of category labels. The E-MIMLSVM+ algorithm is a classical classification algorithm that uses degenerate ideas in the multi-instance multi-label learning framework. It can′t use unlabeled samples to learn and cause poor generalization ability. This paper uses semi-supervised support vector machine to implement the algorithm. The improved algorithm can use a small number of labeled samples and a large number of unlabeled samples to learn, which helps to discover the hidden structure information inside the sample set and understand the true distribution of the sample set. It can be seen from the comparison experiment that the improved algorithm effectively improve the generalization performance of the classifier.