



@book {2504,
	title = {A probabilistic model for the cooperative modular neural network},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {2652},
	year = {2003},
	note = {Cited By (since 1996): 2Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Alexandre, L.A.; Departamento de Inform{\'a}tica, Universidade da Beira Interior, Covilh{\~a}, Portugal; email: lfbaa@di.ubi.ptReferences: Auda, G., Kamel, M., Modular neural network classifiers: A comparative study (1998) J. Intel. Robotic Systems, pp. 117-129;De Bollivier, M., Gallinari, P., Thiria, S., Cooperation of neural nets and task decomposition (1991) Int. Joint Conf. on Neural Networks, 2, pp. 573-576. , Seattle, USA; 
Jacobs, R., Jordan, M., Nowlan, S., Hinton, G., Adaptive mixtures of local experts (1991) Neural Computation, pp. 79-87; 
Jacobs, R., Peng, F., Tanner, M., A bayesian approach to model selection in hierarchical mixtures-of-experts architectures (1997) Neural Networks, 10, pp. 231-241; 
Wanas, N., Kamel, M., Auda, G., Karray, F., Feature-based decision aggregation in modular neural network classifiers (1999) Pattern Recognition Letters, 20, pp. 1353-1359; 
Alexandre, L., Campilho, A., Kamel, M., Combining independent and unbiased classifiers using weighted average (2000) Proceedings of the 15th International Conference on Pattern Recognition, 2, pp. 495-498. , Barcelona, Spain, IEEE Press; 
Alexandre, L., Campilho, A., Kamel, M., On combining classifiers using sum and product rules (2001) Pattern Recognition Letters, 22, pp. 1283-1289; 
Kittler, J., Hatef, M., Duin, R., Matas, J., On combining classifiers (1998) IEEE Trans. PAMI, 20, pp. 226-239; 
Auda, G., Kamel, M., CMNN: Cooperative modular neural networks for pattern recognition (1997) Pattern Recognition Letters, 18, pp. 1391-1398; 
Auda, G., Kamel, M., Raafat, H., Voting schemes for cooperative neural network classifiers (1995) IEEE Int. Conference on Neural Networks, 3, pp. 1240-1243. , Australia
},
	month = {2003///},
	pages = {11 - 18},
	abstract = {This paper presents a model for the probability of correct classification for the Cooperative Modular Neural Network (CMNN). The model enables the estimation of the performance of the CMNN using parameters obtained from the data set. The performance estimates for the experiments presented are quite accurate (less than 1\% relative difference). We compare the CMNN with a multi-layer perceptron with equal number of weights and conclude that the CMNN is preferred for complex problems. We also investigate the error introduced by one of the CMNN voting strategies. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-34547529574\&partnerID=40\&md5=fe30a6406b8bc4deb9b0cc36ab961d59},
	author = {Alexandre, L.A. and Campilho, A. and Kamel, M.}
}
