



@conference {2442,
	title = {Neural network classification: Maximizing zero-error density},
	booktitle = {Lecture Notes in Computer ScienceLect. Notes Comput. Sci.},
	series = {Third International Conference on Advances in Patten Recognition, ICAPR 2005},
	volume = {3686},
	year = {2005},
	note = {Conference code: 65875Cited By (since 1996): 2Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Silva, L.M.; Instituto de Engenharia Biom{\'e}dica, Lab. de Sinai e Imagem Biom{\'e}dica, Campus da FEUP, Rua Dr. Roberto Frias, 4200 - 465 Porto, PortugalReferences: 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;Erdogmus, D., Principe, J., Generalized information potential criterion for adaptive system training (2002) IEEE Transactions on Neural Networks, 13 (5), pp. 1035~1044; 
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; 
Santos, J.M., Alexandre, L.A., De Sa, J.M., Optimization of the error entropy minimization algorithm for neural network classification (2004) Intelligent Engineering Systems Through Artificial Neural Networks, 14, pp. 81-86. , C. Dagli, A. Buczak, D. Enke, M. Embrechts, and O. Ersoy, editors, ASME Press Series; 
Silva, L.M., De Sa, J.M., Alexandre, L.A., Neural network classification using shannon{\textquoteright}s entropy (2005) European Symposium on Artificial Neural Networks; 
Jacobs, R., Jordan, M., Nowlan, S., Hinton, G., Adaptive mixtures of local experts (1991) Neural Computation, 3, pp. 79-87; 
Blake, C.L., Merz, C.J., (1998) UCI Repository of Machine Learning Databases, , http://www.ics.uci.edu/~mlearn/MLRepository.html, University of California, Irvine, Dept. of Information and Computer Sciences
},
	month = {2005///},
	pages = {127 - 135},
	address = {Bath},
	abstract = {We propose a new cost function for neural network classification: the error density at the origin. This method provides a simple objective function that can be easily plugged in the usual backpropagation algorithm, giving a simple and efficient learning scheme. Experimental work shows the effectiveness and superiority of the proposed method when compared to the usual mean square error criteria in four well known datasets. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
	keywords = {Algorithms, Backpropagation, Backpropagation algorithm, Classification (of information), Costs, Error analysis, Function evaluation, Learning systems, Mean square error, Neural network classification, Neural networks, Zero-error density},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-27244450344\&partnerID=40\&md5=73ee3c0c073fb3bc7bc85013bf7dcce6},
	author = {Silva, L.M. and Alexandre, L.A. and De S{\'a}, J.M.},
	editor = {Singh S. and Singh M. and Apte C. and Perner P.}
}
