Margin based Active Learning for LVQ Networks

In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation procedure. The proposed active learning strategy aims on an improved generalization ability of the final model. This is achieved by usage of an adaptive query strategy which is more adequate for supervised learning than a simple random approach. Beside an improved generalization ability the method also improves the speed of the learning procedure which is especially beneficial for large data sets with multiple similar items. The algorithm is based on the idea of selecting a query on the borderline of the actual classification. This can be done by considering margins in an extension of learning vector quantization based on an appropriate cost function. The proposed active learning approach is analyzed for two kinds of learning vector quantizers the supervised relevance neural gas and the supervised nearest pr...

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Frank-Michael Schleif, Barbara Hammer, Thomas Villmann
• Keywords: Active Learning, ESANN 2006, ESANN 2007, Improved Generalization Ability, Learning Vector Quantization