



@conference {2390,
	title = {Modular neural network task decomposition via entropic clustering},
	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): 1Export Date: 21 November 2012Source: ScopusArt. No.: 4021410doi: 10.1109/ISDA.2006.198Language of Original Document: EnglishCorrespondence Address: Santos, J.M.; Instituto Superior de Engenharia do Porto, Instituto de Engenharia Biom{\'e}dica, Porto, Portugal; email: jms@isep.ipp.ptReferences: Santos, J.M., Marques de S{\'a}, J., Alexandre, L.A., LEGClust - a clustering algorithm based on layered entropie subgraphs (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence, , submitted;Bauer, E., Kohavi, R., An empirical comparison of voting classification algorithms: Bagging, boosting, and variants (1999) Machine Learning, 36 (1-2), pp. 105-139; 
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Forina, M., Armanino, C., Eigenvector projection and simplified non-linear mapping of fatty acid content of Italian olive oils (1981) Ann. Chim. (Rome), 72, pp. 127-155
},
	month = {2006///},
	pages = {62 - 67},
	address = {Jinan},
	abstract = {The use of monolithic neural networks (such as a multilayer perceptron) has some drawbacks: e.g. slow learning, weight coupling, the black box effect. These can be alleviated by the use of a modular neural network. The creation of a MNN has three steps: task decomposition, module creation and decision integration. In this paper we propose the use of an entropie clustering algorithm as a way of performing task decomposition. We present experiments on several real world classification problems that show the performance of this approach. {\textcopyright} 2006 IEEE.},
	keywords = {Classification (of information), Clustering algorithms, Decision Theory, Entropic clustering, Learning systems, Modular neural network task decomposition, Monolithic neural networks, Neural networks, Weight coupling},
	isbn = {0769525288 (ISBN); 9780769525280 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-34547502901\&partnerID=40\&md5=c784fc5fb020181d30a63f65ed112c41},
	author = {Santos, J.M. and Alexandre, L.A. and De S{\'a}, J.M.}
}
