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Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/7911

Authors: Sandri, L.*
Marzocchi, W.*
Title: Testing the performance of some nonparametric pattern recognition algorithms in realistic cases
Title of journal: Pattern Recognition
Series/Report no.: 3/37 (2004)
Publisher: Elsevier
Issue Date: Mar-2004
DOI: 10.1016/j.patcog.2003.08.009
Keywords: nonparametric pattern recognition
synthetic data
optimal subset of features
volcanological data
Abstract: The success obtained by Statistical Pattern Recognition in many disciplines is certainly related to the quality and availability of many data, normally distributed. However, in other disciplines, the data sets consist of few measurements, often binned, correlated, and not normally distributed. Usually, we do not even know which features have an influence on the process. The main goal of this paper is to evaluate the performance of some nonparametric Pattern Recognition algorithms when applied to such data. Finally we show the results of the application of the four nonparametric statistical pattern recognition techniques to real volcanological data.
Appears in Collections:Papers Published / Papers in press
05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks

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