The inference of tree languages from finite samples: an algebraic approach
Theoretical Computer Science
Approximating grammar probabilities: solution of a conjecture
Journal of the ACM (JACM)
Predicting Protein Secondary Structure Using Stochastic Tree Grammars
Machine Learning - Special issue on learning with probabilistic representations
Computing the relative entropy between regular tree languages
Information Processing Letters
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Stochastic Inference of Regular Tree Languages
Machine Learning
Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Probabilistic k-Testable Tree Languages
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Inducing Probabilistic Grammars by Bayesian Model Merging
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Frequent Itemset Counting Across Multiple Tables
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Learning probabilistic relational models
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
LARS: A learning algorithm for rewriting systems
Machine Learning
Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Learning context-free grammar using improved tabular representation
Applied Soft Computing
A bibliographical study of grammatical inference
Pattern Recognition
Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
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This paper addresses the problem of learning a statistical distribution of data in a relational database. Data we want to focus on are represented with trees which are a quite natural way to represent structured information. These trees are used afterwards to infer a stochastic tree automaton, using a well-known grammatical inference algorithm. We propose two extensions of this algorithm: use of sorts and generalization of the infered automaton according to a local criterion. We show on some experiments that our approach scales with large databases and both improves the predictive power of the learned model and the convergence of the learning algorithm.