Witoelar A, Biehl M, Ghosh A, Hammer B. Learning dynamics and robustness of vector quantization and neural gas. Neurocomputing. 2008;71(7-9):1210-1219
Hammer B, Strickert M, Villmann T. Learning Vector Quantization for Multimodal Data. In: Dorronsoro ...
Hammer B, Hofmann D, Schleif F-M, Zhu X. Learning vector quantization for (dis-)similarities. NeuroC...
Kohonen's Learning Vector Quantization (LVQ) is modified by attributing training counters to ea...
Various alternatives have been developed to improve the winner-takes-all (WTA) mechanism in vector q...
A large variety of machine learning models which aim at vector quantization have been proposed. Howe...
Biehl M, Ghosh A, Hammer B. Learning vector quantization: The dynamics of winner-takes-all algorithm...
Villmann T, Hammer B. Supervised Neural Gas for Learning Vector Quantization. In: Polani D, Kim J, M...
Villmann T, Schleif F-M, Hammer B. Supervised Neural Gas and Relevance Learning in Learning Vector Q...
Biehl M, Gosh A, Hammer B. The dynamics of Learning Vector Quantization. In: Verleysen M, ed. ESANN'...
The statistical physics of off-learning is applied to winner-takes-all (WTA) and rank-based vector q...
Schleif F-M, Villmann T. Neural Maps and Learning Vector Quantization - Theory and Applications. In:...
Villmann T, Hammer B, Biehl M. Some theoretical aspects of the neural gas vector quantizer. In: Bieh...
International audienceThis paper focuses on the possibility of enabling vector quantization learning...
Schneider P, Biehl M, Hammer B. Adaptive relevance matrices in learning vector quantization. Neural ...
Hammer B, Strickert M, Villmann T. Learning Vector Quantization for Multimodal Data. In: Dorronsoro ...
Hammer B, Hofmann D, Schleif F-M, Zhu X. Learning vector quantization for (dis-)similarities. NeuroC...
Kohonen's Learning Vector Quantization (LVQ) is modified by attributing training counters to ea...
Various alternatives have been developed to improve the winner-takes-all (WTA) mechanism in vector q...
A large variety of machine learning models which aim at vector quantization have been proposed. Howe...
Biehl M, Ghosh A, Hammer B. Learning vector quantization: The dynamics of winner-takes-all algorithm...
Villmann T, Hammer B. Supervised Neural Gas for Learning Vector Quantization. In: Polani D, Kim J, M...
Villmann T, Schleif F-M, Hammer B. Supervised Neural Gas and Relevance Learning in Learning Vector Q...
Biehl M, Gosh A, Hammer B. The dynamics of Learning Vector Quantization. In: Verleysen M, ed. ESANN'...
The statistical physics of off-learning is applied to winner-takes-all (WTA) and rank-based vector q...
Schleif F-M, Villmann T. Neural Maps and Learning Vector Quantization - Theory and Applications. In:...
Villmann T, Hammer B, Biehl M. Some theoretical aspects of the neural gas vector quantizer. In: Bieh...
International audienceThis paper focuses on the possibility of enabling vector quantization learning...
Schneider P, Biehl M, Hammer B. Adaptive relevance matrices in learning vector quantization. Neural ...
Hammer B, Strickert M, Villmann T. Learning Vector Quantization for Multimodal Data. In: Dorronsoro ...
Hammer B, Hofmann D, Schleif F-M, Zhu X. Learning vector quantization for (dis-)similarities. NeuroC...
Kohonen's Learning Vector Quantization (LVQ) is modified by attributing training counters to ea...
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