Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
Distance learning in discriminative vector quantization
Neural Computation
Adaptive relevance matrices in learning vector quantization
Neural Computation
Regularization in matrix relevance learning
IEEE Transactions on Neural Networks
Window-based example selection in learning vector quantization
Neural Computation
Divergence-based vector quantization
Neural Computation
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
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Prototype based classification approaches are powerful classifiers for class discrimination of vectorial data. Famous examples are learning vector quantization models (LVQ) and support vector machines (SVMs). In this paper we propose the application of kernel distances in LVQ such that the LVQ-algorithm can handle the data in a topologically equivalent data space compared to the feature mapping space in SVMs. Further, we provide strategies to force the LVQ-prototypes to be class border sensitive. In this way an alternative to SVMs based on Hebbian learning is established. After presenting the theoretical background, we demonstrate the abilities of the model for an illustrative toy example and for the more challenging task of classification of Wilson's disease patients according to their neurophysiological impairments.