2012 MultiInstanceMultiLabelLearning

• (Zhou, Zhang, et al., 2012) ⇒ Zhi-Hua Zhou, Min-Ling Zhang, Sheng-Jun Huang, and Yu-Feng Li. (2012). “Multi-instance Multi-label Learning.” In: Artificial Intelligence Journal, 176(1). doi:10.1016/j.artint.2011.10.002

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+Multi-instance+Multi-label+Learning
• http://dl.acm.org/citation.cfm?id=2069768.2069972&preflayout=flat#citedby

Quotes

Abstract

In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 MultiInstanceMultiLabelLearning	Zhi-Hua Zhou Min-Ling Zhang Sheng-Jun Huang Yu-Feng Li			Multi-instance Multi-label Learning				10.1016/j.artint.2011.10.002		2012