Classification of chestnuts with feature selection by noise resilient classifiers.
作者： Elena Roglia , Rossella Cancelliere , Rosa Meo
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摘要: In this paper we solve the problem of classifying chestnut plants according to their place origin. We compare results obtained by state art classifiers, among which, MLP, RBF, SVM, C4.5 decision tree and random forest. determine which features are meaningful for classification, achievable classification accuracy these classifiers families with available how much robust noise. Among neural networks show greatest robustness
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