摘要
Multi-instance multi-label learning(MIML) is a new machine learning framework where one data object is described by multiple instances and associated with multiple class labels.During the past few years,many MIML algorithms have been developed and many applications have been described.However,there lacks theoretical exploration to the learnability of MIML.In this paper,through proving a generalization bound for multi-instance single-label learner and viewing MIML as a number of multi-instance single-label learning subtasks with the correlation among the labels,we show that the MIML hypothesis class constructed from a multi-instance single-label hypothesis class is PAC-learnable.
Multi-instance multi-label learning （MIML） is a new machine learning framework where one data object is described by multiple instances and associated with multiple class labels. During the past few years, many MIML algorithms have been developed and many applications have been described. However, there lacks theoretical exploration to the learnability of MIML. In this paper, through proving a generalization bound for multi-instance single-label learner and viewing MIML as a number of multi-instance single-label learning subtasks with the correlation among the labels, we show that the M1ML hypothesis class constructed from a multi-instance single-label hypothesis class is PAC-learnable.
基金
supported by the National Basic Research Program of China(2010CB327903)
the National Natural Science Foundation of China(61073097,61021062)
关键词
机器学习
多实例
标签
易学
数据对象
ML算法
machine learning, learnability, multi-instance multi-label learning （MIML）
