Machine Learning - Special issue on learning with probabilistic representations
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A New Bayesian Network Structure for Classification Tasks
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Learning Bayesian Belief Network Classifiers: Algorithms and System
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Time and sample efficient discovery of Markov blankets and direct causal relations
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Bayesian networks and information retrieval: an introduction to the special issue
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Speculative Markov Blanket Discovery for Optimal Feature Selection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A Bayesian belief network for IT implementation decision support
Decision Support Systems
A chain-model genetic algorithm for Bayesian network structure learning
Proceedings of the 9th annual conference on Genetic and evolutionary computation
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm
Decision Support Systems
Inference in the Promedas Medical Expert System
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Bayesian belief network for box-office performance: A case study on Korean movies
Expert Systems with Applications: An International Journal
ConVeS: a context verification framework for object recognition system
Proceedings of the 2009 conference on Information Science, Technology and Applications
A comparison of novel and state-of-the-art polynomial Bayesian network learning algorithms
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Building classifiers using Bayesian networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Artificial Intelligence in Medicine
Application of Bayesian Belief Network in Reliable Analysis for Video Deinterlacing
IEEE Transactions on Consumer Electronics
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This paper presents a comparative study of Bayesian belief network structure learning algorithms with a view to identify a suitable algorithm for modeling the contextual relations among objects typically found in natural imagery. Four popular structure learning algorithms are compared: two constraint-based algorithms (PC proposed by Spirtes and Glymour and Fast Incremental Association Markov Blanket proposed by Yaramakala and Margaritis), a score-based algorithm (Hill Climbing as implemented by Daly), and a hybrid algorithm (Max-Min Hill Climbing proposed by Tsamardinos et al.). Contrary to the belief regarding the superiority of constraint-based approaches, our empirical results show that a score-based approach performs better on our context dataset in terms of prediction power and learning time. The hybrid algorithm could achieve similar prediction performance as the score-based approach, but requires longer time to learn the desired network. Another interesting fact the study has revealed is the existence of strong correspondence between the linear correlation pattern within the dataset and the edges found in the learned networks.