Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Relational learning with statistical predicate invention: better models for hypertext
Machine Learning - Special issue on inducive logic programming
Learning Logical Definitions from Relations
Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Probabilistic First-Order Classification
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Lookahead and Discretization in ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Maximum Entropy Modeling with Clausal Constraints
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Simple Estimators for Relational Bayesian Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Statistical Relational Learning for Document Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ACM SIGKDD Explorations Newsletter
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Naive Bayesian Classification of Structured Data
Machine Learning
nFOIL: integrating Naïve Bayes and FOIL
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint 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 integrated approach to learning bayesian networks of rules
ECML'05 Proceedings of the 16th European conference on Machine Learning
Discriminative structure and parameter learning for Markov logic networks
Proceedings of the 25th international conference on Machine learning
Discriminative Structure Learning of Markov Logic Networks
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
An l 1 Regularization Framework for Optimal Rule Combination
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Trading expressivity for efficiency in statistical relational learning: Ph.D. thesis abstract
ACM SIGKDD Explorations Newsletter
Context-sensitive refinements for stochastic optimisation algorithms in inductive logic programming
Artificial Intelligence Review
Boosting learning and inference in Markov logic through metaheuristics
Applied Intelligence
Hybrid learning system for adaptive complex event processing
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
ACM Transactions on Asian Language Information Processing (TALIP)
Learning probabilistic description logics: a framework and algorithms
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
A new relational Tri-training system with adaptive data editing for inductive logic programming
Knowledge-Based Systems
Reducing the size of databases for multirelational classification: a subgraph-based approach
Journal of Intelligent Information Systems
Reducing examples in relational learning with bounded-treewidth hypotheses
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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A novel relational learning approach that tightly integrates the naïve Bayes learning scheme with the inductive logic programming rule-learner FOIL is presented. In contrast to previous combinations that have employed naïve Bayes only for post-processing the rule sets, the presented approach employs the naïve Bayes criterion to guide its search directly. The proposed technique is implemented in the NFOIL and TFOIL systems, which employ standard naïve Bayes and tree augmented naïve Bayes models respectively. We show that these integrated approaches to probabilistic model and rule learning outp erform post-processing approaches. They also yield significantly more accurate models than si mple rule learning and are competitive with more sophisticated ILP systems.