Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
A tutorial on ν-support vector machines: Research Articles
Applied Stochastic Models in Business and Industry - Statistical Learning
Neural Computation
Fast learning in networks of locally-tuned processing units
Neural Computation
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
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Against the background of classification in data mining tasks typically various aspects of accuracy, and often also of model size are considered so far. The aspect of interpretability is just beginning to gain general attention. This paper evaluates all three of these aspects within the context of several computational intelligence based paradigms for high-dimensional spectral classification of data acquired by hyperspectral imaging and Raman spectroscopy. It is focused on state-of-the-art paradigms of a number of different concepts, such as prototype based, kernel based, and support vector based approaches. Since the application point of view is emphasized, three real-world datasets are the basis of the presented study.