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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Vydáno v:	Pattern recognition Ročník 37; číslo 3; s. 447 - 461
Hlavní autoři:	Sandri, Laura, Marzocchi, Warner
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.03.2004
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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