Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis. - Thomas Villmann; Marika Kaden; Mehrdad Mohannazadeh Bakhtiari; Andrea Villmann (ICAISC (2), 2019)

A Mathematical Model for Optimum Error-Reject Trade-Off for Learning of Secure Classification Models in the Presence of Label Noise During Training. - Seyedfakhredin Musavishavazi; Mehrdad Mohannazadeh Bakhtiari; Thomas Villmann (ICAISC (1), 2020)

The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach. - Marika Kaden; Ronny Schubert; Mehrdad Mohannazadeh Bakhtiari; Lucas Schwarz; Thomas Villmann (ESANN, 2021)

Classification by Components Including Chow's Reject Option. - Mehrdad Mohannazadeh Bakhtiari; Thomas Villmann (ICONIP (4), 2022)

Learning Vector Quantization in Context of Information Bottleneck Theory. - Mehrdad Mohannazadeh Bakhtiari; Daniel Staps; Thomas Villmann (ESANN, 2023)

An Interpretable Two-Layered Neural Network Structure-Based on Component-Wise Reasoning. - Mehrdad Mohannazadeh Bakhtiari; Thomas Villmann (ICAISC (1), 2023)

The Geometry of Decision Borders Between Affine Space Prototypes for Nearest Prototype Classifiers. - Mehrdad Mohannazadeh Bakhtiari; Andrea Villmann; Thomas Villmann (ICAISC (1), 2023)