Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/176CBB13-43FD-4D79-9F27-C297C4B1F452 | ||
| Year | 2000 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | 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 | ||
| Appears in Collections |
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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