



@book {2262,
	title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {6112 LNCS},
	year = {2010},
	note = {Conference code: 81097Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Campilho, A.; University of Porto, Faculty of Engineering, Institute of Biomedical Engineering, 4200-465 Porto, Portugal; email: campilho@fe.up.pt},
	month = {2010///},
	pages = {V - VI},
	address = {Povoa de Varzim},
	isbn = {03029743 (ISSN); 3642137741 (ISBN); 9783642137747 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77955371702\&partnerID=40\&md5=39645611f42b004904f05cef3083d93d},
	author = {Campilho, A. and Kamel, M.}
}
@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; 
Jacobs, R., Jordan, M., Nowlan, S., Hinton, G., Adaptive mixtures of local experts (1991) Neural Computation, pp. 79-87; 
Auda, G., Kamel, M., Modular neural network classifiers: A comparative study (1998) J. Intel. Robotic Systems, pp. 117-129; 
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.}
}
@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.}
}
@article {2542,
	title = {On combining classifiers using sum and product rules},
	journal = {Pattern Recognition LettersPattern Recogn. Lett.},
	volume = {22},
	year = {2001},
	note = {Cited By (since 1996): 51Export Date: 21 November 2012Source: Scopusdoi: 10.1016/S0167-8655(01)00073-3Language of Original Document: EnglishCorrespondence Address: Alexandre, L.A.; INEB-Inst. de Engenharia Biomedica, Rua Dr. Roberto Frias s/n, Porto 4200-465, Portugal; email: lfbaa@noe.ubi.ptReferences: Alexandre, L., Campilho, A., Kamel, M., Combining independent and unbiased classifiers using weighted average (2000) Proc. 15th Internat. Conf. on Pattern Recognition, 2. , IEEE Press, Barcelona, Spain;Bishop, C., (1995) Neural Networks for Pattern Recognition, , Oxford University Press, Oxford; 
Blake, C., Keogh, E., Merz, C., (1998) UCI Repository of Machine Learning Databases, , http://www.ics.uci.edu/~mlearn/MLRepository.html; 
Demuth, H., Beale, M., (1998) Neural Network Toolbox User{\textquoteright}s Guide, , The MathWorks, Inc; 
Hansen, L., Salamon, P., Neural network ensembles (1990) IEEE Trans. PAMI, 12 (10), pp. 993-1001; 
Hashem, S., Optimal linear combinations of neural networks (1997) Neural Networks, 10 (4), pp. 599-614; 
Kittler, J., Hatef, M., Duin, R., Combining classifiers (1996) Proc. Internat. Conf. on Pattern Recognition{\textquoteright}96; 
Kittler, J., Hatef, M., Duin, R., Matas, J., On combining classifiers (1998) IEEE Trans. PAMI, 20 (3), pp. 226-239; 
Opitz, D., Maclin, R., Popular ensemble methods: An empirical study (1999) J. Artif. Intell. Res, 11, pp. 169-198; 
Richard, M., Lippmann, R., Neural network classifiers estimate Bayesian a posteriori probabilities (1991) Neural Comput, 3, pp. 461-483; 
Rogova, G., Combining the results of several neural network classifiers (1994) Neural Networks, 7 (5), pp. 777-781; 
Tax, D., Duin, R., Breukelen, M., Comparison between product and mean classifier combination rules (1997) Workshop on Statistical Techniques in Pattern Recognition, , Prague Czech Republic; 
Ueda, N., Optimal linear combination of neural networks for improving classification performance (2000) IEEE Trans. PAMI, 22 (2), pp. 207-215
},
	month = {2001///},
	pages = {1283 - 1289},
	abstract = {This paper presents a comparative study of the performance of arithmetic and geometric means as rules to combine multiple classifiers. For problems with two classes, we prove that these combination rules are equivalent when using two classifiers and the sum of the estimates of the a posteriori probabilities is equal to one. We also prove that the case of a two class problem and a combination of two classifiers is the only one where such equivalence occurs. We present experiments illustrating the equivalence of the rules under the above mentioned assumptions. {\textcopyright} 2001 Elsevier Science B.V. All rights reserved.},
	keywords = {artificial neural network, Classification, Classifier fusion, Combinatorial mathematics, Combining classifiers, Equivalence classes, Estimation, K nearest-neighbours, Multiple classifiers, Neural networks, Pattern recognition, probability},
	isbn = {01678655 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0034875843\&partnerID=40\&md5=becfe9c0c4a7d97f7bbaf33f3059ef8f},
	author = {Alexandre, L.A. and Campilho, A.C. and Kamel, M.}
}
