Redes Bayesianas

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Referencias • J. A. Lozano, P. Larrañaga (1998). Applying genetic algorithms to search for the best hierarchical clustering of a dataset. Pattern Recognition Letters 20, 911-918 • P. Larrañaga, C. Kuijpers, R. Murga, I. Inza, S. Dizdarevich (1999) Evolutionary algorithms for the travelling salesman problem: A review of representations and operators. Artificial Intelligence Review, 13, 129–170 • J. A. Lozano, P. Larrañaga, M. Graña, F. X. Albizuri (1999) Genetic algorithms: bridging the convergence gap. Theoretical Computer Science, 229, 11-22 • J. M. Peña, J. A. Lozano, P. Larrañaga (1999) An empirical comparison of four initialization methods for the k-means algorithm. Pattern Recognition Letters, 20, 1027–1040 • C. González, J. A. Lozano, P. Larrañaga (2000) Analyzing the PBIL Algorithm by Means of Discrete Dynamical Systems, Complex Systems, Vol. 12, No. 4, 465–479 Redes Bayesianas – p.86/91
Referencias • B. Sierra, N. Serrano, P. Larrañaga, E. J. Plasencia, I. Inza, J. J. Jimenez, J. M. De la Rosa, M. J. Mora (2001) Using Bayesian networks in the construction of a bi-level multiclassifier. A case study using intensive care unit patients data Artificial Intelligence in Medicine, 22, 233–248 • P. Larrañaga, R. Etxeberria, J. A. Lozano, J. M. Peña (2000). Combinatorial optimization by learning and simulation of Bayesian networks. Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, 343–352 • I. Inza, P. Larrañaga, R. Etxeberria, B. Sierra (2000) Feature Subset Selection by Bayesian networks based optimization. Artificial Intelligence, 123, 157–184 • I. Inza, M. Merino, P. Larrañaga, J. Quiroga, B. Sierra, M. Girala (2001) Feature subset selection by genetic algorithms and estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS. Artificial Intelligence in Medicine, 23–2, 187–205 • P. Larrañaga, J. A. Lozano (2001) (eds.) Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publishers Redes Bayesianas – p.87/91
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Redes Bayesianas Pedro Larrañaga D
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1. Introducción • Limitación de
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1. Introducción • Grafos acícli
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1. Introducción • X = (X 1 , . .
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2. Independencia condicional Distri
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2. Independencia condicional Probab
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2. Independencia condicional p(x|y,
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2. Independencia condicional ¿I(X,
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3. Factorización Factorización de
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3. Factorización Red Bayesiana •
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4. Propagación de la evidencia Mé
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6. Aprendizaje estructural • Apre
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6. Aprendizaje estructural Score:
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6. Aprendizaje estructural Algoritm
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6. Aprendizaje estructural Diferent
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6. Aprendizaje estructural Clasific
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