中文摘要：

多标记学习主要解决一个样本可以同时属于多个类别的问题,它广泛适用于图像场景分类、文本分类等任务.在传统的多标记学习中,分类器往往需要利用大量具有完整标记的训练样本才能获得较好的分类性能,然而,在很多现实应用中又往往只能获得少量标记不完整的训练样本.为了更好地利用这些弱标记训练样本,提出一种针对弱标记的直推式多标记分类方法,它可以通过标记误差加权来补全样本标记,同时也能更好地利用弱标记样本提高分类性能.实验结果表明,该方法在弱标记情况下的图像场景分类任务上具有较好的性能提高.

英文摘要：

Multi-label learning deals with the problems when each object can be assigned to multiple categories simultaneously,which is ubiquitous in many real world applications,such as text classification,image scene classification and bioinformatics,etc.In traditional multi-label learning methods,classifiers are usually required to utilize a large amount of fully labeled training data in order to obtain good performances for multi-label classifications.However,in many real world tasks,obtaining partially labeled（weak labeled） training data is often much easier and costs less efforts than obtaining a large amount of fully labeled training data.To alleviate the assumption of large amount fully labeled training data used by traditional multi-label learning methods,the authors propose a new multi-label learning method for weak labeling（TML-WL）.By reweighting the error functions on positive and negative labels of weak labeled data,TML-WL method can effectively utilize the weak labeled training data to replenish the missing labels.TML-WL method can also use the weak labeled training data to improve the classification performances on unlabeled data.Empirical studies on the real-world application of image scene classification show that the proposed method can significantly improve the performance of multi-label learning when the training data are weak labeled.