@conference{10902/9607,
year = {2015},
url = {http://hdl.handle.net/10902/9607},
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.},
publisher = {JMLR-},
publisher = {Microtome Publishing},
publisher = {JMLR: workshop and conference proceedings, 2015, 37, 2427–2435},
publisher = {32nd International Conference on Machine Learning, Lille, France, 2015},
title = {Multi-instance multi-label learning in the presence of novel class instances},
author = {Pham, Anh T. and Raich, Raviv and Fern, Xiaoli Z. and Pérez Arriaga, Jesús},
}