Search Results for author:
Found 10 papers, 2 papers with code
Feature Relevance Bounds for Ordinal Regression
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i. e. the prediction of ordered classes.
Galaxy classification: A machine learning analysis of GAMA catalogue data
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.
Prototype-based classifiers in the presence of concept drift: A modelling framework
We present a modelling framework for the investigation of prototype-based classifiers in non-stationary environments.
On-line learning dynamics of ReLU neural networks using statistical physics techniques
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.
Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation
We study layered neural networks of rectified linear units (ReLU) in a modelling framework for stochastic training processes.
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information
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.
Supervised Learning in the Presence of Concept Drift: A modelling framework
We present a modelling framework for the investigation of supervised learning in non-stationary environments.
Complex-valued embeddings of generic proximity data
As an information preserving alternative, we propose a complex-valued vector embedding of proximity data.
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets
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.
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces
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.
