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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Bandwidth Selection in Kernel Density Estimators for Multiple-Resolution Classification
verfasst von : Mateusz Kobos, Jacek Mańdziuk
Erschienen in: Artificial Intelligence and Soft Computing
Verlag: Springer Berlin Heidelberg
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We consider a problem of selection of parameters in a classifier based on the average of kernel density estimators where each estimator corresponds to a different data “resolution”. The selection is based on adjusting parameters of the estimators to minimize a substitute of the misclassification ratio. We experimentally compare the misclassification ratio and parameters selected for benchmark data sets by the introduced algorithm with these values of the algorithm’s baseline version. In order to place the classification results in a wider context, we compare them with results of other popular classifiers.