68 citations · 302 across the 26 of their papers we have counts for
27 papers
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces
Sofie Lövdal, Michael Biehl
We introduce and investigate the iterated application of Generalized Matrix Learning Vector Quantizaton for the analysis of feature relevances in classification problems, as well a…
A machine learning based approach to gravitational lens identification with the International LOFAR Telescope
S. Rezaei, J. P. McKean, M. Biehl +2
We present a novel machine learning based approach for detecting galaxy-scale gravitational lenses from interferometric data, specifically those taken with the International LOFAR…
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets
Sreejita Ghosh, Elizabeth S. Baranowski, Michael Biehl +3
Application of interpretable machine learning techniques on medical datasets facilitate early and fast diagnoses, along with getting deeper insight into the data. Furthermore, the…
Complex-valued embeddings of generic proximity data
Maximilian Münch, Michiel Straat, Michael Biehl +1
Proximities are at the heart of almost all machine learning methods. If the input data are given as numerical vectors of equal lengths, euclidean distance, or a Hilbertian inner pr…
Supervised Learning in the Presence of Concept Drift: A modelling framework
Michiel Straat, Fthi Abadi, Zhuoyun Kan +3
We present a modelling framework for the investigation of supervised learning in non-stationary environments. Specifically, we model two example types of learning systems: prototyp…
Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation
Elisa Oostwal, Michiel Straat, Michael Biehl
We study layered neural networks of rectified linear units (ReLU) in a modelling framework for stochastic training processes. The comparison with sigmoidal activation functions is…