P. Larranaga, R. Etxeberria, J.A. Lozano, and J.M. Pena, Combinatorial optimization by learning and simulation of Bayesian networks, Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (Standford), 2000, pp. 343--352. Home/Search Document Not in Database Summary Related Articles Check |
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Using A Priori Knowledge To Create - Probabilistic Models For (Correct)
....specified by an expert. The FDA algorithm is designed to work with problems that are decomposable into independent parts. This work has been extended to incorporate learning with low complexity networks and Junction Trees [36] 37] The Bayesian Optimization Algorithm (BOA) and related work [13][30][39] 40] is the closest method to the optimization techniques presented here. The model used in BOA is able to represent arbitrary dependencies. When general Bayesian Networks are used for modeling, the scoring function used to determine the quality of the network plays a vital role in finding ....
Larraaga, P., Etxeberria, R., Lozano. J.A., and Pea, J.M. (2000), "Combinatorial Optimization by Learning and Simulation of Bayesian Networks", Proceedings of the Conference in Uncertainty in Artificial Intelligence, Morgan Kauffman Publishers, pp 343-352.
Variable Search Space For Software Testing - Ramn Sagarna Jose (2003) Self-citation (Lozano) (Correct)
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P. Larraaga, R. Etxeberria, J.A. Lozano, and J.M. Pea, "Combinatorial optimization by learning and simulation of Bayesian networks ", Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, pp. 343-352, 2000.
Dealing with Software Testing via Estimation of.. - Sagarna, Lozano.. (2003) (1 citation) Self-citation (Lozano) (Correct)
....statistics of order greater than two and, obviously, require the elicitation of a structure. Thus, the factorization is as follows: p l (x i jpa i ) 4) where pa i are the instantiations of Pa i , the set of variables on which X i depends. The Estimation of Bayesian Network Algorithm (EBNA) [15] is an example of multivariate EDA. A Bayesian network (BN) 4] is learnt from l 1 to estimate p l (x) A BN is a pair (S; where S is a directed acyclic graph representing the (in)dependencies between the variables and is the set of conditional probability values needed to de ne the joint ....
P. Larraaga, R. Etxeberria, J. A. Lozano, J. M. Pea, Combinatorial optimization by learning and simulation of Bayesian networks, Proceedings of the Sixteenth Conference on Uncertainty in Arti cial Intelligence, pages 343-352, Stanford, 2000.
Mathematical Modelling of UMDAc Algorithm with.. - González.. (2002) (1 citation) Self-citation (Larranaga Lozano) (Correct)
....this problem, several authors have proposed di#erent algorithms where simplified assumptions concerning the conditional dependencies between the variables of the joint probability distribution are made. A review of the di#erent approaches in the combinatorial and numerical fields can be found in [8, 9, 10, 16]. During recent years much e#ort has been devoted to creating new EDAs and EDA applications. However this development has not been accompanied by mathematical analysis. There are very few works devoted to a mathematical modelling of EDAs in the literature. Reviewing the literature, we can ....
P. Larranaga, R. Etxeberria, J. A. Lozano, and J. M. Pena. Combinatorial Optimization by Learning and Simulation of Bayesian Networks. In C. Boutilier and M. Goldszmidt, editors, Proceedings of Uncertainty in Artificial Intelligence, UAI-2000.
Analyzing the PBIL Algorithm by Means of Discrete.. - Gonzalez, Lozano.. (1 citation) Self-citation (Larranaga Lozano) (Correct)
....authors have proposed di#erent algorithms where simplified assumptions concerning the conditional (in)dependencies between the variables of the joint probability distribution are made. A review of the di#erent approaches in the combinatorial and numerical fields can be seen in Larranaga et al. [3, 4] and Pelikan et al. 5] The Population Based Incremental Learning algorithm (PBIL) can be considered as an EDA, as proposed by Baluja [6] PBIL supposes that all the variables are independent. At each step of the algorithm a probability vector is maintained. This vector is sampled # times to ....
P. Larranaga, R. Etxeberria, J. A. Lozano and J. M. Pena, "Combinatorial Optimization by Learning and Simulation of Bayesian Networks," in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, UAI-2000, edited by C. Boutilier and M. Goldszmidt, 343-352 (Morgan Kaufmann, 2000).
Research on the Improvement of Efficiency of EDAs for Optimization - Paul (2004) (Correct)
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P. Larranaga, R. Etxeberria, J.A. Lozano, and J.M. Pena, Combinatorial optimization by learning and simulation of Bayesian networks, Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (Standford), 2000, pp. 343--352.
Linear and Combinatorial Optimizations by Estimation of.. - Paul, Iba (2002) (Correct)
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Larranaga,P., Etxeberria, R., Lozano,J.A. and Pena, J.M.(2000). Combinatorial Optimization by learning and simulation of Bayesian networks. In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, Stanford, 343-352.
Probabilistic Methods in Landscape Analysis: Phase.. - Gao, Culberson (Correct)
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Larranaga, P., Etxeberria, R., Lozano, J.A., Pena, J.M.: Combinatorial optimization by learning and simulation of bayesian networks. In Boutilier, C., Goldszmidt, M., eds.: Proceedings of the Sixteenth Conference on Uncertainty in Articial Intelligence, Morgan Kaufmann (2002) 343--352
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