Forschung / Research
Theory of neural vector quantization
- self-organizing maps
- neural gas
- (generalized) learning vector quantization
- semi-supervised learning
- functional principal component learning
- fuzzy classification and clustering
Information theoretic learning
- magnification and magnification control in neural maps
- divergence based vector quantization
- information optimum learning
Non-standard metrics in vector quantization
- relevance learning
- functional metrics and functional relevance learning
- adaptive transformation invariant metrics
- generalized metrics and dissimilarities for machine learning
- kernel based metrics for online learning
Prototype-based classification learning vector quantization
- adaptive classifcation learning by learning vector quantization (LVQ)
- robust and secure classification by LVQ
- LVQ classication based on statistical evaluation measures beyond accuracy (precision, specificity, F-measure etc.)
- classification task dependent outlier detection
- class representive and class border sensitive classification learning
Evolutionary algorithms
- topologically structure island models
- neighborhood cooperativeness for recombination
High-dimensional data analysis
- hyperspectral remote sensing data analysis
- mass spectrometry data analysis in medicine and biology
- class visualization and projection