Search Results for author: Michael Biehl

Found 9 papers, 1 papers with code

Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets

no code implementations4 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.

Interpretable Machine Learning

Supervised Learning in the Presence of Concept Drift: A modelling framework

no code implementations21 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.

Quantization

Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information

no code implementations10 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.

regression

Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation

no code implementations16 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.

Galaxy classification: A machine learning analysis of GAMA catalogue data

no code implementations18 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.

BIG-bench Machine Learning Classification +2

Prototype-based classifiers in the presence of concept drift: A modelling framework

no code implementations18 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.

Quantization

On-line learning dynamics of ReLU neural networks using statistical physics techniques

no code implementations18 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.

Feature Relevance Bounds for Ordinal Regression

1 code implementation20 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.

regression

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