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 and thereby increase speed and accuracy of the model. Our 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 performance of the query algorithm is demonstrated on real life data.

• Type: Journal
• Year: 2007
• Where: IJON
• Authors: Frank-Michael Schleif, Barbara Hammer, Thomas Villmann
• Keywords: Active Learning, Appropriate Cost Function, IJON 2007, Local Prototype-based Learning