【摘要】 多示例多标签学习框架是一种针对解决多义性问题而提出的新型机器学习框架,在多示例多标签学习框架中,一个对象是用一组示例集合来表示,并且和一组类别标签相关联。E-MIMLSVM~+算法是多示例多标签学习框架中利用退化思想的经典分类算法,针对其无法利用无标签样本进行学习从而造成泛化能力差等问题,使用半监督支持向量机对该算法进行改进。改进后的算法可以利用少量有标签样本和大量没有标签的样本进行学习,有助于发现样本集内部隐藏的结构信息,了解样本集的真实分布情况。通过对比实验可以看出,改进后的算法有效提高了分类器的泛化性能。更多还原

【Abstract】 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 se-mi-supervised support vector machine to implement the algorithm. The improved algorithm can use a small number of labeled sam-ples 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 algo-rithm effectively improve the generalization performance of the classifier.更多还原