Self-organizing maps
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
Soft nearest prototype classification
IEEE Transactions on Neural Networks
Margin-based active learning for LVQ networks
Neurocomputing
Dynamics and Generalization Ability of LVQ Algorithms
The Journal of Machine Learning Research
Prototype based fuzzy classification in clinical proteomics
International Journal of Approximate Reasoning
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We propose a new method for the construction of nearest prototype classifiers which is based on a Gaussian mixture approach interpreted as an annealed version of Learning Vector Quantization. Thereby we allow the adaptation of the underling metric which is useful in proteomic research. The algorithm performs a gradient descent on a cost function adapted from soft nearest prototype classification. We investigate the properties of the algorithm and assess its performance on two clinical cancer data sets. Results show that the algorithm performs reliable with respect to alternative state of the art classifiers.