On the behavior of artificial neural network classifiers inhigh-dimensional spaces
Hamamoto, Y.
Uchimura, S.
Tomita, S.
Fac. of Eng., Yamaguchi Univ., Ube;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: May 1996
Volume: 18,
Issue: 5
On page(s): 571-574
ISSN: 0162-8828
References Cited: 28
CODEN: ITPIDJ
INSPEC Accession Number: 5288463
Digital Object Identifier: 10.1109/34.494648
Current Version Published: 2002-08-06
Abstract
It is widely believed in the pattern recognition field that when a
fixed number of training samples is used to design a classifier, the
generalization error of the classifier tends to increase as the number
of features gets larger. In this paper, we discuss the generalization
error of the artificial neural network (ANN) classifiers in
high-dimensional spaces, under a practical condition that the ratio of
the training sample size to the dimensionality is small. Experimental
results show that the generalization error of ANN classifiers seems much
less sensitive to the feature size than 1-NN, Parzen and quadratic
classifiers
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