@inproceedings {pub3533,
	title = {Efficient Accuracy Estimation for Instance-Based Incremental Active Learning},
	author = {Christian  Limberg AND Heiko Wersing AND Helge Ritter},
	year = {2018},
	month = {April},
	abstract = {Estimating systems accuracy is crucial for applications of in-
cremental learning. In this paper, we introduce the Distogram Estimation
(DGE) approach to estimate the accuracy of instance-based classifiers. By
calculating relative distances to samples it is possible to train an offline
regression model, capable of predicting the classifiers accuracy on unseen
data. Our approach requires only a few supervised samples for training and
can instantaneously be applied on unseen data afterwards. We evaluate
our method on five benchmark data sets and for a robot object recognition
task. Our algorithm clearly outperforms two baseline methods both for
random and active selection of incremental training examples.},
	publisher = {i6doc},
	booktitle = {European Symposium on Artificial Neural Networks},
	city = {Bruges},
	pages = {171-176}
}