Self-organizing maps
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
Neural maps in remote sensing image analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Statistical Classification and Visualization of MALDI-Imaging Data
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Cancer informatics by prototype networks in mass spectrometry
Artificial Intelligence in Medicine
Computing and Visualization in Science
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
To reject or not to reject: that is the question-an answer in caseof neural classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
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The analysis and classification of data is a common task in multiple fields of experimental research such as bioinformatics, medicine, satellite remote sensing or chemometrics leading to new challenges for an appropriate analysis. For this purpose different machine learning methods have been proposed. These methods usually do not provide information about the reliability of the classification. This, however, is a common requirement in, e.g. medicine and biology. In this line the present contribution offers an approach to enhance classifiers with reliability estimates in the context of prototype vector quantization. This extension can also be used to optimize precision or recall of the classifier system and to determine items which are not classifiable. This can lead to significantly improved classification results. The method is exemplarily presented on satellite remote spectral data but is applicable to a wider range of data sets.