V. Tresp, Committee Machines Chapter in Handbook of neural network signal processing, 2002.

H. Drucker, Fast Committee Machines for Regression and Classification, KDD-97 Proceedings, 1997.

F. Girosi, M. Jones, and T. Poggio, Regularization Theory and Neural Networks Architectures, Neural Computation, vol.26, issue.3, pp.219-269, 1995.
DOI : 10.1016/0893-6080(90)90004-5
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.9258

T. Evgeniou, M. Pontil, and T. Poggio, Regularization Networks and Support Vector Machines, Advances in Computational Mathematics, vol.13, issue.1, pp.1-50, 2000.
DOI : 10.1023/A:1018946025316

T. Poggio and S. Smale, The Mathematics of Learning: Dealing with Data, 2005 International Conference on Neural Networks and Brain, pp.537-544, 2003.
DOI : 10.1109/ICNNB.2005.1614546

C. M. Bishop, Neural Networks for Pattern Recognition, 1995.

A. K. Jain, R. P. Duin, and J. Mao, Statistical pattern recognition: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.1, pp.4-37, 2000.
DOI : 10.1109/34.824819
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.123.8151

L. Wang and X. Fu, Data Mining with Computational Intelligence, 2005.

H. Kashima, T. Ide, T. Kato, and M. Sugiyama, Recent Advances and Trends in Large-Scale Kernel Methods, IEICE Transactions on Information and Systems, vol.92, issue.7, pp.1338-1353, 2009.
DOI : 10.1587/transinf.E92.D.1338

A. Prodromidis and P. Chan, Meta-learning in a distributed data mining system: Issues and approaches, Proceedings of the Fourteenth International Conference on Machine Learning, pp.211-218, 1998.

C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Zhu, Tools for privacy preserving distributed data mining, ACM SIGKDD Explorations Newsletter, vol.4, issue.2, pp.1-7, 2003.
DOI : 10.1145/772862.772867
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.417

M. Kantarcioglu and J. Vaidya, Privacy-Preserving Naive Bayes Classifier for Horizontally Partitioned Data, IEEE Workshop on Privacy-Preserving Data Mining, 2003.

X. Yi and Y. Zhang, Privacy-preserving naive Bayes classification on distributed data via semi-trusted mixers, Information Systems, vol.34, issue.3, pp.371-380, 2009.
DOI : 10.1016/j.is.2008.11.001

L. Xiong, S. Chitti, and L. Liu, k nearest neighbour classification across multiple private databases, Proceedings of the ACM Fifteenth Conference on Information and Knowledge Management, 2006.
DOI : 10.1145/1183614.1183757
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.126.3190

L. K. Hansen and P. Salamon, Neural network ensembles, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.10, pp.993-1001, 1990.
DOI : 10.1109/34.58871
URL : http://orbit.dtu.dk/en/publications/neural-network-ensembles(492f6c68-703a-4b6d-97bb-8509d817d00f).html

M. P. Perrone and L. N. Cooper, When networks disagree: Ensemble methods for hybrid neural networks, in Neural Networks for Speech and Image Processing, 1993.
DOI : 10.1142/9789812795885_0025

A. Krogh and J. Vedelsby, Neural networks ensembles, cross validation and active learning, Advances in Neural Information Processing Systems, 1995.

G. Wilson, Parallel Programming for Scientists and Engineers, 1995.