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. © 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Testing the performance of some nonparametric pattern recognition algorithms in realistic cases / Sandri, L.; Marzocchi, W.. - In: PATTERN RECOGNITION. - ISSN 0031-3203. - 37:3(2004), pp. 447-461. [10.1016/j.patcog.2003.08.009]
Testing the performance of some nonparametric pattern recognition algorithms in realistic cases
Marzocchi, W.
2004
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. © 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
