@inproceedings {pub4263,
	title = {Prototype-Based Online Learning on Homogeneously Labeled Streaming Data},
	author = {Christian  Limberg AND Heiko Wersing AND Helge Ritter AND Jan Philip Goepfert},
	year = {2020},
	month = {September},
	abstract = {Algorithms in machine learning commonly require training
data to be independent and identically distributed. This assumption is
not always valid, e. g. in online learning, when data becomes available
in homogeneously labeled blocks, which can severely impede especially
instance-based learning algorithms. In this work, we analyze and visu-
alize this issue, and we propose and evaluate strategies for Learning
Vector Quantization to compensate for homogeneously labeled blocks.
We achieve considerably improved results in this difficult setting.
},
	publisher = {Springer, LNCS, LNAI, LNBI},
	booktitle = {International Conference on Artificial Neural Networks (ICANN)}
}