Multi-Instance Learning from Supervised View
Zhi-Hua Zhou
【期刊名称】《计算机科学技术学报（英文版）》 【年(卷),期】2006(021)005
【摘要】In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances,and the task is to predict the labels of unseen bags. This paper studies multi-instance learning from the view of supervised learning. First, by analyzing some representative learning algorithms, this paper shows that multi-instance learners can be derived from supervised learners by shifting their focuses from the discrimination on the instances to the discrimination on the bags. Second, considering that ensemble learning paradigms can effectively enhance supervised learners, this paper proposes to build multi-instance
ensembles
to
solve
multi-instance
problems.
Experiments on a real-world benchmark test show that ensemble learning paradigms can significantly enhance multi-instance learners. 【总页数】10页(800-809)
【关键词】machine learning;multi-instance learning;supervised learning;ensemble learning;multi-instance ensemble 【作者】Zhi-Hua Zhou
【作者单位】National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, P.R. China
Multi-Instance Learning from Supervised View
