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.

本文提出了 MIML（多实例多标签学习）框架，可以更方便、更自然地表示具有多重语义含义的复杂对象，并基于简单的退化策略提出了 MimlBoost 和 MimlSvm 算法。同时，本文还提出了用于解决 MIML 问题的 D-MimlSvm、InsDif 和 SubCod 算法，并在实验中证明了它们的优异表现。