Stability and Accuracy of Feature Selection Methods on Datasets of Varying Data Complexity

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Margin based Active Learning for LVQ Networks

Schleif F-M, Hammer B, Villmann T. Margin based Active Learning for LVQ Networks. In: Verleysen M, ed. Proc. Of European Symposium on Artificial Neural Networks. Brussels, Belgium: d-side publications; 2006: 539-544

Margin based Active Learning for LVQ Networks

Schleif F-M, Hammer B, Villmann T. Margin based Active Learning for LVQ Networks. Neurocomputing. 2007;70(7-9):1215-1224

Margin based Active Learning for LVQ Networks

Abstract. 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.