Machine Learning - special issue on inductive logic programming
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Learning Logical Definitions from Relations
Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Modeling with Clausal Constraints
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Statistical Relational Learning for Document Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Discriminative versus generative parameter and structure learning of Bayesian network classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Entity Resolution with Markov Logic
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Integrating Naïve Bayes and FOIL
The Journal of Machine Learning Research
Bottom-up learning of Markov logic network structure
Proceedings of the 24th international conference on Machine learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Efficient Weight Learning for Markov Logic Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Memory-efficient inference in relational domains
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
nFOIL: integrating Naïve Bayes and FOIL
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Discriminative training of Markov logic networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A general method for reducing the complexity of relational inference and its application to MCMC
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Probabilistic inductive logic programming: theory and applications
Probabilistic inductive logic programming: theory and applications
An integrated approach to learning bayesian networks of rules
ECML'05 Proceedings of the 16th European conference on Machine Learning
Max-Margin Weight Learning for Markov Logic Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Generative Structure Learning for Markov Logic Networks
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Discriminative Markov logic network structure learning based on propositionalization and X2-test
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Boosting learning and inference in Markov logic through metaheuristics
Applied Intelligence
Online structure learning for Markov logic networks
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
On the combination of logical and probabilistic models for information analysis
Applied Intelligence
Generative structure learning for Markov logic networks based on graph of predicates
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Learning the structure of probabilistic logic programs
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Location-based reasoning about complex multi-agent behavior
Journal of Artificial Intelligence Research
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
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Markov Logic Networks (MLNs) combine Markov networks and first-order logic by attaching weights to first-order formulas and viewing these as templates for features of Markov networks. Learning the structure of MLNs is performed by state-of-the-art methods by maximizing the likelihood of a relational database. This can lead to suboptimal results given prediction tasks. On the other hand better results in prediction problems have been achieved by discriminative learning of MLNs weights given a certain structure. In this paper we propose an algorithm for learning the structure of MLNs discriminatively by maximimizing the conditional likelihood of the query predicates instead of the joint likelihood of all predicates. The algorithm chooses the structures by maximizing conditional likelihood and sets the parameters by maximum likelihood. Experiments in two real-world domains show that the proposed algorithm improves over the state-of-the-art discriminative weight learning algorithm for MLNs in terms of conditional likelihood. We also compare the proposed algorithm with the state-of-the-art generative structure learning algorithm for MLNs and confirm the results in [22] showing that for small datasets the generative algorithm is competitive, while for larger datasets the discriminative algorithm outperfoms the generative one.