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Bouckaert, R. (1994). Properties of bayesian belief network learning algorithms, Proc. of Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp. 102–110. 36 Bayesian Network Bozdogan, H. (1987). Model selection and akaike’s information criteria (AIC): The general theory and its analytical extentions, Psychometrika 52: 354–370. Charniak, E. (1991). Bayesian networks without tears, AI Magazine 12(4): 50–63. , Bell, D. A. & Liu, W. (2002). Learning belief networks from data: An information theory based approach, Artificial Intelligence 1-2: 43–90.

1993). Optimal mutation rates in genetic search, Proc. of Int. Conf. on Genetic Algorithms, Morgan Kaufmann, San Mateo (CA), pp. 2–8. , Tabbone, S. & Nourrissier, P. (2007). A bayesian classifier for symbol recognition, Proc. of GREC. , Bull, D. R. & Martin, R. R. (1993). A sequential niche technique for multimodal function optimization, Evolutionary Computation 1(2): 101–125. Beinlich, I. , Suermondt, H. , Chavez, R. M. & Cooper, G. F. (1989). The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks, Proc.

Springer Verlag, Berlin, pp. 247–256. , Russell, S. J. & Kanazawa, K. (1997). Adaptive probabilistic networks with hidden variables, Machine Learning 29(2-3): 213–244. , Inza, I. & Larrañaga, P. (2003). , Int. Jour. of Int. Syst. 18(2): 205–220. Bouckaert, R. (1994). Properties of bayesian belief network learning algorithms, Proc. of Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp. 102–110. 36 Bayesian Network Bozdogan, H. (1987). Model selection and akaike’s information criteria (AIC): The general theory and its analytical extentions, Psychometrika 52: 354–370.

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