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On overfitting, generalization, and randomly expanded training sets

Karystinos Georgios, Pados Dimitris A.

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URI: http://purl.tuc.gr/dl/dias/176CBB13-43FD-4D79-9F27-C297C4B1F452
Έτος 2000
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
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Λεπτομέρειες
Βιβλιογραφική Αναφορά G. N. Karystinos and D. A. Pados, “On overfitting, generalization, and randomly expanded training sets,” IEEE Transactions on Neural Networks, vol. 11, no. 5, pp. 1050-1057, Sept. 2000. doi: 10.1109/72.870038 https://doi.org/10.1109/72.870038
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Περίληψη

An algorithmic procedure is developed for the random expansion of a given training set to combat overfitting and improve the generalization ability of backpropagation trained multilayer perceptrons (MLPs). The training set is K-means clustered and locally most entropic colored Gaussian joint input-output probability density function estimates are formed per cluster. The number of clusters is chosen such that the resulting overall colored Gaussian mixture exhibits minimum differential entropy upon global cross-validated shaping. Numerical studies on real data and synthetic data examples drawn from the literature illustrate and support these theoretical developments

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