Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An Optimal Set of Discriminant Vectors
IEEE Transactions on Computers
IEEE Transactions on Computers
Prediction of commodity prices in rapidly changing environments
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
k-nearest neighbors directed noise injection in multilayer perceptron training
IEEE Transactions on Neural Networks
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We investigate accuracy, neural network complexity and sample size problem in multilayer perceptron (MLP) based (neuro-linear) feature extraction. For feature extraction we use weighted sums calculated in hidden units of the MLP based classifier. Extracted features are utilized for data visualisation in 2D and 3D spaces and interactive formation of the pattern classes. We show analytically how complexity of feature extraction algorithm depends on the number of hidden units. Sample size – complexity relations investigated in this paper showed that reliability of the neuro-linear feature extraction could become extremely low if number of new features is too high. Visual interactive inspection of data projection may help an investigator to look differently at the forecasting problem of the financial time series.