Testing the performance of some nonparametric pattern recognition algorithms in realistic cases

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. Usual...

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Bibliographic Details
Published in:Pattern recognition Vol. 37; no. 3; pp. 447 - 461
Main Authors: Sandri, Laura, Marzocchi, Warner
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.03.2004
Subjects:
Nonparametric Pattern Recognition
Optimal subset of features
Synthetic data
Volcanological data
Nonparametric Pattern Recognition
Optimal subset of features
Volcanological data
Synthetic data
ISSN:0031-3203, 1873-5142
Online Access:Get full text
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Summary: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.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2003.08.009