@inproceedings {pub2819,
	title = {Certainty-based Prototype Insertion/Deletion for Prototype-based Classification with Metric Adaptation},
	author = {Lydia Fischer AND Barbara Hammer AND Heiko Wersing},
	year = {2015},
	abstract = {We propose an extension of prototype-based classification models to automatically adjust model complexity, thus offering a powerful technique for online, incremental learning tasks. The incremental technique is based on the notion of the certainty of an observed classification. Unlike previous work,  we can incorporate matrix learning into the framework by relying on the cost function of generalised learning vector quantisation (GLVQ) for prototype insertion, deletion, as well as training. In several benchmarks, we demonstrate that the proposed method provides comparable results to offline counterparts and an incremental support vector machine, while enabling a better control of the required memory.},
	publisher = {i6doc.com},
	booktitle = {23th European Symposium on Artificial Neural Networks ESANN 2015},
	editor = {Michel Verleysen},
	city = {Bruges, Belgium},
	pages = {7-12}
}