Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data

The paper deals with the application of the novel generalized relevance learning vector quantization based classification algorithm in comparison to the unified maximum separability analysis as a special variant of support vector machine algorithms. The algorithms are compared and their performance on real life data, taken from clinical studies, is demonstrated. It is shown that the vector quantization classifier gives competitive results in comparison to the considered support vector machine algorithm and shows the recently theoretical proven equivalence in classification capability of both paradigms.