



@book {2462,
	title = {Bounds for the Average Generalization Error of the Mixture of Experts 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 = {3138},
	year = {2004},
	note = {Cited By (since 1996): 2Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Alexandre, L.A.; Networks and Multimedia Group, IT, Covilh{\~a}, Portugal; email: lfbaa@di.ubi.ptReferences: Gu, H., Takahashi, H., Towards more practical average bounds on supervised learning (1996) IEEE Trans. Neural Networks, 7, pp. 953-968;Vapnik, V., (1999) The Nature of Statistical Learning Theory., , Springer; 
Haykin, S., (1999) Neural Networks: A Comprehensive Foundation, 2nd Edition., , Prentice Hall; 
Alexandre, L., Campilho, A., Kamel, M., Average error bound for the mixture of experts MNN architecture (2002) Proceedings of the 12th Portuguese Conference on Pattern Recognition, , Aveiro, Portugal; 
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Jordan, M., Jacobs, R., Hierarchical mixture of experts and the em algorithm (1994) Neural Computation, pp. 181-214; 
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; 
De Marques Sa, J., Pattern Recognition: Concepts (2001) Methods and Applications, , Springer; 
Blake, C., Keogh, E., Merz, C., (1998) UCI repository of machine learning databases, , http://www.ics.uci.edu/~mlearn/MLRepository.html; 
Gu, H., Takahashi, H., Estimating learning curves of concept learning (1997) Neural Networks, 10, pp. 1089-1102; 
Bishop, C., (1995) Neural Networks for Pattern Recognition., , Oxford University Press; 
Riedmiller, M., Braun, H., A direct adaptive method for faster backpropagation learning: The RPROP algorithm (1993) IEEE International Conference on Neural Networks, pp. 586-591. , San Francisco
},
	month = {2004///},
	pages = {618 - 625},
	abstract = {In this paper we derive an upper bound for the average-case generalization error of the mixture of experts modular neural network, based on an average-case generalization error bound for an isolated neural network. By doing this we also generalize a previous bound for this architecture that was restricted to special problems. We also present a correction factor for the original average generalization error, that was empirically obtained, that yields more accurate error bounds for the 6 data sets used in the experiments. These experiments illustrate the validity of the derived error bound for the mixture of experts modular neural network and show how it can be used in practice. {\textcopyright} Springer-Verlag 2004.},
	keywords = {Generalization error bounds, Mixture of experts, Modular neural networks},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-35048839786\&partnerID=40\&md5=10183183d399e4055c331416faef909e},
	author = {Alexandre, L.A. and Campilho, A. and Kamel, M.}
}
