



@book {2331,
	title = {The influence of the risk functional in data classification with MLPs},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5163 LNCS},
	year = {2008},
	note = {Conference code: 73798Cited By (since 1996): 2Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-540-87536-9_20Language of Original Document: EnglishCorrespondence Address: Silva, L. M.; INEB -Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUP, Rua Dr. Roberto Frias, 4200 - 465 Porto, Portugal; email: lmsilva@fe.up.ptReferences: Silva, L.M., Alexandre, L.A., Marques de S{\'a}, J.: Data classification with multilayer perceptrons using a generalized error function. Neural Networks (2008) doi:10.1016/j.neunet.2008.04,004Erdogmus, D., Principe, J.C., Generalized information potential criterion for adaptive system training (2002) IEEE Transactions on Neural Networks, 13 (5), pp. 1035-1044;Erdogmus, D., Principe, J.C., An Error-Entropy Minimization Algorithm for Supervised Training of Nonlinear Adaptive Systems (2002) IEEE Transactions on Signal Processing, 50 (7), pp. 1780-1786; 
Santos, J.M., Alexandre, L.A., Marques de S{\'a}, J., The Error Entropy Minimization Algorithm for Neural Network Classification (2004) Int. Conf. on Recent Advances in Soft Computing, , Nottingham, United Kingdom; 
Duda, R., Hart, P., Stork, D., (2001) Pattern Classification, , Wiley-Interscience, Chichester; 
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Baum, E.B., Haussler, D., What size net gives valid generalization? (1990) Neural Computation, 1 (1), pp. 151-160; 
Santos, J.M., Marques de S{\'a}, J., Alexandre, L.A., Neural Networks Trained with the EEM Algorithm: Tuning the Smoothing Parameter (2005) WSEAS Transactions on Systems, 4 (4), pp. 295-300; 
Marques de S{\'a}, J., (2007) Using SPSS, STATISTICA, MATLAB and R, , 2nd edn. Springer, Heidelberg; 
Embrechts, M.J., Szymanski, B., Sternickel, M., Introduction to Scientific Data Mining: Direct Kernel Methods and Applications (2004) Computationally Intelligent Hybrid Systems, pp. 317-363. , ch. 10, pp, Wiley Interscience, Chichester; 
Bennett, K.P., Embrechts, M.J., An Optimization Perspective on Kernel Partial Least Squares Regression (2003) Models and Applications. NATO Science Series, 3, pp. 227-249. , Advances in Learning Theory: Methods, 190, pp, IOS Press, Amsterdam; 
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Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J., (2003) Least Squares Support Vector Machines, , World Scientific Pub. Co, Singapore; 
Keerthi, S.S., Shevade, S.K., SMO algorithm for least squares SVM formulations (2003) Neural Computation, 15, pp. 487-507; 
Cristianini, N., Shawe-Taylor, J., (2000) Support Vector Machines and Other KernelBased Learning Methods, , Cambridge University Press, Cambridge; 
Vapnik, V., (1998) Statistical Learning Theory, , Wiley-Interscience, Chichester; 
Sch{\"o}lkopf, B., Smola, A.J., (2002) Learning with Kernels, , MIT Press, Cambridge
},
	month = {2008///},
	pages = {185 - 194},
	address = {Prague},
	abstract = {We investigate the capability of multilayer perceptrons using specific risk functionals attaining the minimum probability of error (optimal performance) achievable by the class of mappings implemented by the multilayer perceptron (MLP). For that purpose we have carried out a large set of experiments using different risk functionals and datasets. The experiments were rigorously controlled so that any performance difference could only be attributed to the different risk functional being used. Statistical analysis was also conducted in a careful way. From the several conclusions that can be drawn from our experimental results it is worth to emphasize that a risk functional based on a specially tuned exponentially weighted distance attained the best performance in a large variety of datasets. As to the issue of attaining the minimum probability of error we also carried out classification experiments using non-MLP classifiers that implement complex mappings and are known to provide the best results until this date. These experiments have provided evidence that at least in many cases, by using an adequate risk functional, it will be possible to reach the optimal performance. {\textcopyright} Springer-Verlag Berlin Heidelberg 2008.},
	keywords = {Backpropagation, Complex mapping, Data classification, Data sets, Electric loads, Experiments, Functionals, Minimum probability of error, MLP classifiers, Multi layer perceptron, Multi-layer perceptrons, Multilayer neural networks, Multilayers, Optimal performance, Pattern recognition systems, Probability density function, risks, Statistical analysis, Weighted distance},
	isbn = {03029743 (ISSN); 3540875352 (ISBN); 9783540875352 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-58849129862\&partnerID=40\&md5=d9d0d90a540a21b191eebbf1956ed6e9},
	author = {Silva, L.M. and Embrechts, M. and Santos, J.M. and De S{\'a}, J.M.}
}
