[PDF][PDF] Aspects in classification learning-Review of recent developments in Learning Vector Quantization

M Kaden, M Lange, D Nebel, M Riedel… - … of Computing and …, 2014‏ - sciendo.com
Classification is one of the most frequent tasks in machine learning. However, the variety of
classification tasks as well as classifier methods is huge. Thus the question is coming up …

Median variants of learning vector quantization for learning of dissimilarity data

D Nebel, B Hammer, K Frohberg, T Villmann - Neurocomputing, 2015‏ - Elsevier
Exemplar based techniques such as affinity propagation represent data in terms of typical
exemplars. This has two benefits:(i) the resulting models are directly interpretable by …

[PDF][PDF] Biomedical data analysis in translational research: Integration of expert knowledge and interpretable models

G Bhanot, M Biehl, T Villmann… - 25th European Symposium …, 2017‏ - research.rug.nl
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 …

[PDF][PDF] Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization.

M Kaden, W Hermann, T Villmann - ESANN, 2014‏ - Citeseer
We propose a framework for classification learning based on generalized learning vector
quantization using statistical quality measures as cost function. Statistical measures like the …

Adaptive conformal semi-supervised vector quantization for dissimilarity data

X Zhu, FM Schleif, B Hammer - Pattern Recognition Letters, 2014‏ - Elsevier
Existing semi-supervised learning algorithms focus on vectorial data given in Euclidean
space. But many real life data are non-metric, given as (dis-) similarities which are not widely …

A median variant of generalized learning vector quantization

D Nebel, B Hammer, T Villmann - International Conference on Neural …, 2013‏ - Springer
We introduce a median variant of the Generalized Learning Vector Quantization (GLVQ)
algorithm. Thus, GLVQ can be used for classification problem learning, for which only …

[PDF][PDF] A framework for optimization of statistical classification measures based on generalized learning vector quantization

M Kaden, T Villmann - Machine Learning Reports, 2013‏ - Citeseer
We propose a framework for classi cation learning based on generalized learning vector
quantization using statistical quality measures as cost function. Statistical measures like the …

Semi-Supervised Learning Vector Quantization method enhanced with regularization for anomaly detection in air conditioning time-series data

I Andriushchenko - 2016‏ - aaltodoc.aalto.fi
Researchers of semi-supervised learning methods have been develo** the family of
Learning Vector Quantization models which originated from the well-known Self-Organizing …