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Effect of the feature vector size on the generalization error: the case of MLPNN and RBFNN classifiers

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3 Author(s)
Malek, J.E. ; Electron. & Micro-Electron. Lab., Fac. of Sci. of Monastir, Tunisia ; Alimi, A.M. ; Tourki, R.

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

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Pattern Recognition, 2000. Proceedings. 15th International Conference on  (Volume:2 )

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