



@article {2335,
	title = {Data classification with multilayer perceptrons using a generalized error function},
	journal = {Neural NetworksNeural Netw.},
	volume = {21},
	year = {2008},
	note = {Cited By (since 1996): 6Export Date: 21 November 2012Source: ScopusCODEN: NNETEdoi: 10.1016/j.neunet.2008.04.004PubMed ID: 18572384Language of Original Document: EnglishCorrespondence Address: Silva, L.M.; INEB-Instituto de Engenharia Biom{\'e}dica, Porto, Portugal; email: lmsilva@fe.up.ptReferences: Baum, E.B., Wilczek, F., Supervised learning of probability distributions by neural networks (1987) NIPS, pp. 52-61. , Anderson D.Z. (Ed), American Institute of Physics;Bishop, C., (1995) Neural networks for pattern recognition, , Oxford University Press; 
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Gish, H. (1990). A probabilistic approach to the understanding and training of neural network classifiers. In Proceedings of the 1990 IEEE international conference on acoustics, speech, and signal processingHampshire, J., Waibel, A., A novel objective function for improved phoneme recognition using time-delay neural networks (1990) IEEE Transactions on Neural Networks, 1 (2), pp. 216-228; 
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Jacobs, R., Jordan, M., Nowlan, S., Hinton, G., Adaptive mixtures of local experts (1991) Neural Computation, 3, pp. 79-87; 
M{\o}ller, M. (1993). Efficient training of feed-forward neural networks. Ph.D. thesis. Computer Science Department - Aarhus UniversityMarques de S{\'a}, J., (2001) Pattern recognition: Concepts, methods and applications, , Springer Verlag; 
Matsuoka, K., \& Yi, J. (1991). Backpropagation based on the logarithmic error function and elimination of local minima. In Proceedings of the 1990 IEEE international joint conference on neural networksRichard, M., Lippmann, R., Neural network classifiers estimate bayesian a posteriori probabilities (1991) Neural Computation, 3, pp. 461-483; 
Santos, J., Alexandre, L., \& Marques~de S{\'a}, J. (2004). The error entropy minimization algorithm for neural network classification. In int. conf. on recent advances in soft computingSilva, L., Alexandre, L., Marques de S{\'a}, J., Neural network classification: Maximizing zero-error density (2005) LNCS, 3686, pp. 127-135. , ICAPR 2005; 
Silva, L., Alexandre, L.A., \& Marques~de S{\'a}, J. (2006). New developments of the Z-EDM algorithm. In Proceedings of the sixth international conference intelligent systems design and applications: Vol.~1 (pp. 1067-1072)Silva, L., Marques~de S{\'a}, J., \& Alexandre, L. (2005). Neural network classification using Shannon{\textquoteright}s entropy. In European symposium on artificial neural networksSilverman, B., (1986) Density estimation for statistics and data analysis, , Chapman \& Hall; 
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},
	month = {2008///},
	pages = {1302 - 1310},
	abstract = {The learning process of a multilayer perceptron requires the optimization of an error function E (y, t) comparing the predicted output, y, and the observed target, t. We review some usual error functions, analyze their mathematical properties for data classification purposes, and introduce a new one, E                        Exp, inspired by the Z-EDM algorithm that we have recently proposed. An important property of E
                        Exp is its ability to emulate the behavior of other error functions by the sole adjustment of a real-valued parameter. In other words, E
                        Exp is a sort of generalized error function embodying complementary features of other functions. The experimental results show that the flexibility of the new, generalized, error function allows one to obtain the best results achievable with the other functions with a performance improvement in some cases. {\textcopyright} 2008 Elsevier Ltd. All rights reserved.
},
	keywords = {algorithm, Algorithms, analytical error, article, calculation, Classification, Complementary features, Cybernetics, data analysis, Data classification, Data classifications, Data Interpretation, Statistical, Electric loads, entropy, Error functions, learning, Learning processes, mathematical analysis, Mathematical properties, Models, Statistical, Multilayer neural networks, Multilayer perceptron, Multilayer perceptrons, Multilayers, Neural networks, Neural Networks (Computer), Pattern recognition systems, perceptron, Performance improvements, priority journal, Probability density function},
	isbn = {08936080 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-54449100428\&partnerID=40\&md5=30f8de18c0272f4a440395045bd3c164},
	author = {Silva, L.M. and Marques de S{\'a}, J. and Alexandre, L.A.}
}
