Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
复制标题
通过极限学习机改进多实例多标签学习
DOI：
10.3390/app6060160
复制
发表时间：
2016-05
期刊：
影响因子：
--
通讯作者：
Bin Zhang
中科院分区：
文献类型：
--
作者：
Ying Yin;Yuhai Zhao;Chengguang Li;Bin Zhang
Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1) the user-specific parameter for the number of clusters may incur the effective problem; (2) SVM may bring a high computational cost when utilized as the classifier builder. In this paper, we propose an algorithm, namely multi-instance multi-label (MIML)-extreme learning machine (ELM), to address the problems. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. Extensive experiments have been conducted on real datasets and synthetic datasets. The results show that MIMLELM tends to achieve better generalization performance at a higher learning speed.
登录
查看更多内容
DOI：
--
发表时间：
2004
期刊：
--
影响因子：
--
作者：
通讯作者：
--
影响因子：
7.5
作者：
Schapire, RE;Singer, Y
通讯作者：
Singer, Y
DOI：
10.5860/choice.28-5119
发表时间：
1990
期刊：
--
影响因子：
--
作者：
G. A. Edgar
通讯作者：
G. A. Edgar
影响因子：
6
作者：
Huang, Guang-Bin;Zhu, Qin-Yu;Siew, Chee-Kheong
通讯作者：
Siew, Chee-Kheong
DOI：
10.7551/mitpress/7503.003.0206
发表时间：
2007
期刊：
--
影响因子：
--
作者：
B. Scholkopf;J. Platt;T. Hofmann
通讯作者：
B. Scholkopf;J. Platt;T. Hofmann