@inproceedings {pub3649,
	title = {Improving Active Learning by Avoiding Ambiguous Samples},
	author = {Christian  Limberg AND Heiko Wersing AND Helge Ritter},
	year = {2018},
	month = {October},
	abstract = {If label information in a classification task is expensive, it can
be beneficial to use active learning to get the most informative samples
to label by a human. However, there can be samples which are mean-
ingless to the human or recorded wrongly. If these samples are near the
classifier{\textquoteright}s decision boundary, they are queried repeatedly for labeling.
This is inefficient for training because the human can not label these
samples correctly and this may lower human acceptance. We introduce
an approach to compensate the problem of ambiguous samples by ex-
cluding clustered samples from labeling. We compare this approach to
other state-of-the-art methods. We further show that we can improve the
accuracy in active learning and reduce the number of ambiguous samples
queried while training.},
	publisher = {Springer},
	booktitle = {International Conference on Artificial Neural Networks}
}