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- W2140974300 abstract "In pattern recognition literature, it is well known that a finite number of training samples cause practical difficulties in designing a classifier. Moreover, the generalization error of the classifier tends to increase as the number of features gets large. We study the generalization error of several classifiers (MLPNN, RBFNN, K NN) in high dimensional spaces, under a practical condition: the ratio of the training sample to the dimensionality is small. Experimental results show that the generalization error of neuronal classifiers decreases as a function of dimensionality while it increases for statistical classifiers." @default.
- W2140974300 created "2016-06-24" @default.
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- W2140974300 date "2002-11-11" @default.
- W2140974300 modified "2023-09-23" @default.
- W2140974300 title "Effect of the feature vector size on the generalization error: the case of MLPNN and RBFNN classifiers" @default.
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- W2140974300 doi "https://doi.org/10.1109/icpr.2000.906154" @default.
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