no code implementations • 23 Jan 2024 • 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 as for the construction of class-discriminative subspaces.
1 code implementation • 4 Jun 2022 • Sreejita Ghosh, Elizabeth S. Baranowski, Michael Biehl, Wiebke Arlt, Peter Tino, Kerstin Bunte
Medical datasets face common issues such as heterogeneous measurements, imbalanced classes with limited sample size, and missing data, which hinder the straightforward application of machine learning techniques.
no code implementations • 31 Aug 2020 • Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif
As an information preserving alternative, we propose a complex-valued vector embedding of proximity data.
no code implementations • 21 May 2020 • Michiel Straat, Fthi Abadi, Zhuoyun Kan, Christina Göpfert, Barbara Hammer, Michael Biehl
We present a modelling framework for the investigation of supervised learning in non-stationary environments.
no code implementations • 10 Dec 2019 • Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tino, Barbara Hammer
In this contribution, we focus on feature selection paradigms, which enable us to uncover relevant factors of a given regularity based on a sparse model.
no code implementations • 16 Oct 2019 • Elisa Oostwal, Michiel Straat, Michael Biehl
We study layered neural networks of rectified linear units (ReLU) in a modelling framework for stochastic training processes.
no code implementations • 18 Mar 2019 • Michael Biehl, Fthi Abadi, Christina Göpfert, Barbara Hammer
We present a modelling framework for the investigation of prototype-based classifiers in non-stationary environments.
no code implementations • 18 Mar 2019 • Michiel Straat, Michael Biehl
We introduce exact macroscopic on-line learning dynamics of two-layer neural networks with ReLU units in the form of a system of differential equations, using techniques borrowed from statistical physics.
no code implementations • 18 Mar 2019 • Aleke Nolte, Lingyu Wang, Maciej Bilicki, Benne Holwerda, Michael Biehl
We present a machine learning analysis of five labelled galaxy catalogues from the Galaxy And Mass Assembly (GAMA): The SersicCatVIKING and SersicCatUKIDSS catalogues containing morphological features, the GaussFitSimple catalogue containing spectroscopic features, the MagPhys catalogue including physical parameters for galaxies, and the Lambdar catalogue, which contains photometric measurements.
1 code implementation • 20 Feb 2019 • Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tino, Barbara Hammer
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i. e. the prediction of ordered classes.