



@conference {2389,
	title = {New developments of the Z-EDM algorithm},
	booktitle = {Proceedings - ISDA 2006: Sixth International Conference on Intelligent Systems Design and ApplicationsProc. ISDA Sixth Int. Conf. Intelligent Syst. Design Applic.},
	series = {ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications},
	volume = {1},
	year = {2006},
	note = {Conference code: 69992Cited By (since 1996): 2Export Date: 21 November 2012Source: ScopusArt. No.: 4021587doi: 10.1109/ISDA.2006.204Language of Original Document: EnglishCorrespondence Address: Silva, L.M.; INEB - Instituto de Engenharia Biom{\'e}dica, Porto, Portugal; email: lmsilva@fe.up.ptReferences: Bishop, C., (1995) Neural Networks for Pattern Recognition, , Oxford University Press;Blake, C., Merz, C., (1998) UCI Repository of machine learning databases University of California, , http://www.ics.uci.edu/~mlearn/MLRepository.html, Irvine, Dept. of Information and Computer Sciences; 
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; 
Forina, M., Armanino, C., Eigenvector projection and simplified non-linear mapping of fatty acid content of Italian olive oils (1981) Ann. Chim. (Rome), 50, pp. 127-155; 
Marques de S{\'a}, J., (2001) Pattern Recognition: Concepts, Methods and Applications, , Springer Verlag; 
J. Santos, L. Alexandre, and J. Marques de S{\'a}. The Error Entropy Minimization Algorithm for Neural Network Classification. In Int. Conf. on Recent Advances in Soft Computing, Nottingham, United Kingdom, 2004Silva, 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., Marques de S{\'a}, J., Alexandre, L., Neural Network Classification using Shannon{\textquoteright}s Entropy (2005) European Symposium on Artificial Neural Networks; 
Silverman, B., (1986) Density Estimation for Statistics and Data Analysis, , Chapman \& Hall
},
	month = {2006///},
	pages = {1067 - 1072},
	address = {Jinan},
	abstract = {In this paper we address some open questions on the recently proposed Zero-Error Density Maximization algorithm for MLP training. We propose a new version of the cost function that solves a training problem encountered in previous work and prove that the use of a nonparametric density estimator preserves the optimal solution. Some experiments are reported comparing this cost function to the usual mean-square error and cross entropy cost junctions. {\textcopyright} 2006 IEEE.},
	keywords = {Algorithms, Cost functions, Mean square error, MLP training, Nonparametric density estimators, Optimization, Personnel training, Problem solving, Zero-Error Density Maximization algorithm},
	isbn = {0769525288 (ISBN); 9780769525280 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-34547496988\&partnerID=40\&md5=65cadef393b0843f5b21f6534bc122ff},
	author = {Silva, L.M. and Marques de S{\'a}, J. and Alexandre, L.A.}
}
