T. Hastie, R. Tibshirani, and J. Friedman, The Element of Statistical Learning: Data Mining, Inference, and Prediction, 2009.

L. Silva, J. Marques-de-sá, and L. A. Alexandre, Data classification with multilayer perceptrons using a generalized error function, Neural Networks, vol.21, issue.9, pp.1302-1310, 2008.
DOI : 10.1016/j.neunet.2008.04.004

C. M. Bishop, Neural Networks for Pattern Recognition, 1995.

L. Silva, J. Marques-de-sá, and L. A. Alexandre, The MEE Principle in Data Classification: A Perceptron-Based Analysis, Neural Computation, vol.22, issue.10, pp.2698-2728, 2010.
DOI : 10.1016/0031-3203(81)90094-7

L. Silva, Neural Networks with Error-Density Risk Functionals for Data Classification, 2008.

L. Hubert and P. Arabie, Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-218, 1985.
DOI : 10.1007/978-3-642-69024-2_27

J. M. Santos and S. Ramos, Using a clustering similarity measure for feature selection in high dimensional data sets, 2010 10th International Conference on Intelligent Systems Design and Applications, pp.900-905, 2010.
DOI : 10.1109/ISDA.2010.5687073

V. Vapnik, E. Levin, and Y. C. Le, Measuring the VC-Dimension of a Learning Machine, Neural Computation, vol.1, issue.3, pp.851-876, 1994.
DOI : 10.1137/1116025

X. Shao and W. Li, Measuring the VC-Dimension Using Optimized Experimental Design, Neural Computation, vol.12, issue.8, 1969.
DOI : 10.1162/neco.1994.6.5.851

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.1626

J. M. Santos, Data classification with neural networks and entropic criteria, 2007.

E. A. Wan, Neural network classification: a Bayesian interpretation, IEEE Transactions on Neural Networks, vol.1, issue.4, 1990.
DOI : 10.1109/72.80269

D. Phatak, Relationship between fault tolerance, generalization and the Vapnik- Chervonenkis (VC) dimension of feed-forward ANNs, Proceedings of the International Joint Conference on Neural Networks (IJCNN), 1999.