This paper offers a comprehensive overview of machine learning (ML) methodologies and
algorithms, highlighting their practical applications in the critical domain of water resource …

We present a machine learning analysis of five labelled galaxy catalogues from the Galaxy
And Mass Assembly (GAMA): The SersicCatVIKING and SersicCatUKIDSS catalogues …

When training automated systems, it has been shown to be beneficial to adapt the
representation of data by learning a problem-specific metric. This metric is global. We extend …

We introduce and investigate the iterated application of Generalized Matrix Learning Vector
Quantization for the analysis of feature relevances in classification problems, as well as for …

Advances in machine learning technologies have led to increasingly powerful models in
particular in the context of big data. Yet, many application scenarios demand for robustly …

Feature Selection is one of the most relevant preprocessing techniques in machine learning.
Yet, it is usually only considered in the context of classification tasks. Although many …

We introduce and investigate the iterated application of Generalized Matrix Relevance
Learning for the analysis of feature relevances in classification problems. The suggested …

Most existing feature selection methods are insufficient for analytic purposes as soon as
high dimensional data or redundant sensor signals are dealt with since features can be …

This thesis being the preliminary culmination of my academic journey, I feel it appropriate to
thank a number of people who have been a positive influence along the way. First and …

The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed
research interest in ordinal regression, ie the prediction of ordered classes. Besides model …