Prof. Dr. rer. nat. (habil.) Thomas Villmann

Diminishing Returns - Data Integer Quantization and its Effects on Training Dynamics of Distance Based Classifiers

Domination Reliability Analysis Based on Graph Features Using Generalized Matrix LVQ

Enforcing Feature Sparseness for Reliable Classification by Prototype-Based Models

Evaluation of Rashomon Sets for the Determination of Stable and Plausible Model Explanations

FA(IR)2MA-GLVQ – A hidden-feature-bias mitigation approach for fairness in classification learning based on generalized matrix learning vector quantization