Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Knowledge-based artificial neural networks
Artificial Intelligence
Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Machine Learning - Special issue on learning with probabilistic representations
Foundations of Logic Programming
Foundations of Logic Programming
Machine Learning
The Connectionist Inductive Learning and Logic Programming System
Applied Intelligence
Learning Logical Definitions from Relations
Machine Learning
Theory Refinement of Bayesian Networks with Hidden Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Probabilistic Relational Models
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Towards Combining Inductive Logic Programming with Bayesian Networks
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Learning structure and parameters of stochastic logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Revision of first-order Bayesian classifiers
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
CLP(BN): constraint logic programming for probabilistic knowledge
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Further results of probabilistic first-order revision of theories from examples
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Structured machine learning: the next ten years
Machine Learning
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Mapping and revising Markov logic networks for transfer learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
PFORTE: revising probabilistic FOL theories
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
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Recently, there has been significant work in the integration of probabilistic reasoning with first order logic representations. Learning algorithms for these models have been developed and they all considered modifications in the entire structure. In a previous work we argued that when the theory is approximately correct the use of techniques from theory revision to just modify the structure in places that failed in classification can be a more adequate choice. To score these modifications and choose the best one the log likelihood was used. However, this function was shown not to be well-suited in the propositional Bayesian classification task and instead the conditional log likelihood should be used. In the present paper, we extend this revision system showing the necessity of using specialization operators even when there are no negative examples. Moreover, the results of a theory modified only in places that are responsible for the misclassification of some examples are compared with the one that was modified in the entire structure using three databases and considering four probabilistic score functions, including conditional log likelihood.