Mathematical Characterization of Sophisticated Variants for Relevance Learning in Learning Matrix Quantization Based on Schatten-p-norms. - Andrea Bohnsack; Kristin Domaschke; Marika Kaden; Mandy Lange; Thomas Villmann (ICAISC (1), 2015)

Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning - A Mathematical Characterization. - Thomas Villmann; Marika Kaden; David Nebel; Andrea Bohnsack (ICAISC (2), 2016)

Self-Adjusting Reject Options in Prototype Based Classification. - Thomas Villmann; Marika Kaden; Andrea Bohnsack; J.-M. Villmann; T. Drogies; Sascha Saralajew; Barbara Hammer (WSOM, 2016)

Learning matrix quantization and relevance learning based on Schatten-p-norms. - Andrea Bohnsack; Kristin Domaschke; Marika Kaden; Mandy Lange; Thomas Villmann (Neurocomputing, 2016)

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning. - Thomas Villmann; Andrea Bohnsack; Marika Kaden (J. Artif. Intell. Soft Comput. Res., 2017)