Journal
NEUROCOMPUTING
Volume 131, Issue -, Pages 15-22Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2013.09.048
Keywords
Learning Vector Quantization (LVQ); Radial Basis Function (RBF) networks; Support Vector Machines (SVM); Supervised Neural Gas (SNG); Hyperspectral imaging; Raman spectroscopy
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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. (C) 2013 Elsevier B.V. All rights reserved.
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