Relevance learning in learning vector quantization is a central paradigm for classification
task depending feature weighting and selection. We propose a functional approach to …

In various fields of biomedical research, the availability of electronic data has increased
tremendously. Not only is the amount of disease specific data increasing, but so is its …

We present a framework for distance-based classification of functional data. We consider the
analysis of labeled spectral data by means of Generalized Matrix Relevance Learning …

Electroencephalography (EEG) is a method for recording the electrical activity of the brain. In
the EEG various frequency signals can analyse the brain and the brain's behaviour. EEG …

In this contribution, we consider the classification of time series and similar functional data
which can be represented in complex Fourier and wavelet coefficient space. We apply …

High dimensional data serves as input for a variety of classification tasks. In the case of
spectral information, this data can be understood as discrete sampling of an (unknown) …

Numerous statistical machine learning techniques have been proposed for solving a variety
of classification problems. Prototype-based models, such as standard learning vector …

By combining very steep squashing functions and normalization as parts of the cost function
of generalized learning vector quantization (GLVQ) and its descendants, vector label …

In this contribution, we consider the classification of time-series and similar functional data
which can be represented in complex Fourier coefficient space. We apply versions of …

The increasing computational power and the availability of data have made it possible to
train ever-bigger artificial neural networks. These so-called deep neural networks have been …