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Learning matrix quantization and relevance learning based on Schatten-p-norms., , , , and . Neurocomputing, (2016)Precision-Recall-Optimization in Learning Vector Quantization Classifiers for Improved Medical Classification Systems., , , , and . CIDM, page 71-77. IEEE, (2014)Lateral enhancement in adaptive metric learning for functional data., , , and . Neurocomputing, (2014)Investigating the Influence of CPU Load, Memory Usage and Environmental Conditions on the Jittering of Android Devices., , , , , , and . ICNCC, page 102-106. ACM, (2018)Investigation of Activation Functions for Generalized Learning Vector Quantization., , , , and . WSOM+, volume 976 of Advances in Intelligent Systems and Computing, page 179-188. Springer, (2019)Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning - A Mathematical Characterization., , , and . ICAISC (2), volume 9693 of Lecture Notes in Computer Science, page 125-133. Springer, (2016)Mathematical Characterization of Sophisticated Variants for Relevance Learning in Learning Matrix Quantization Based on Schatten-p-norms., , , , and . ICAISC (1), volume 9119 of Lecture Notes in Computer Science, page 403-414. Springer, (2015)Complex Variants of GLVQ Based on Wirtinger's Calculus., , , , and . WSOM, volume 428 of Advances in Intelligent Systems and Computing, page 293-303. Springer, (2016)Kernelized vector quantization in gradient-descent learning., , and . Neurocomputing, (2015)Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters., , , , and . Comp. Int. and Neurosc., (2013)