@conference {443,
	title = {Multi-instance multi-label learning in the presence of novel class instances},
	booktitle = {32nd International Conference on Machine Learning (ICML)},
	year = {2015},
	month = {July},
	address = {Lille, France},
	abstract = {Multi-instance multi-label learning (MIML) is a framework for learning in the presence of label ambiguity. In MIML, experts provide labels for groups of instances (bags), instead of directly providing a label for every instance. When labeling efforts are focused on a set of target classes, instances outside this set will not be appropriately modeled. For example, ornithologists label bird audio recordings with a list of species present. Other additional sound instances, e.g., a rain drop or a moving vehicle sound, are not labeled. The challenge is due to the fact that for a given bag, the presence or absence of novel instances is latent. In this paper, this problem is addressed using a discriminative probabilistic model that accounts for novel instances. We propose an exact and efficient implementation of the maximum likelihood approach to determine the model parameters and consequently learn an instance-level classifier for all classes including the novel class. Experiments on both synthetic and real datasets illustrate the effectiveness of the proposed approach.},
	url = {http://icml.cc/2015/?page_id=825$\#$SupervisedLearning~I},
	author = {Pham, Anh T. and Raviv Raich and Fern, Xiaoli Z. and P{\'e}rez, Jes{\'u}s}
}
