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2012
Journal Article
Titel
Classification in high-dimensional spectral data - precision vs. interpretability vs. model size
Abstract
This paper evaluates aspects of precision, interpretability and model size of several computational intelligence based classification methods in the context of hyperspectral imaging and Raman spectroscopy. It is focussed on state-of-the-art representative paradigms of a number of different concepts, such as prototype based, kernel based, and support vector based approaches.