Learning nonrecursive definitions of relations with LINUS
EWSL-91 Proceedings of the European working session on learning on Machine learning
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Simple Estimators for Relational Bayesian Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ACM SIGKDD Explorations Newsletter
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Machine Learning
Naive Bayesian Classification of Structured Data
Machine Learning
Exploring optimization of semantic relationship graph for multi-relational Bayesian classification
Decision Support Systems
Boosting tuple propagation in multi-relational classification
Proceedings of the 15th Symposium on International Database Engineering & Applications
Good and bad practices in propositionalisation
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Combining bayesian networks with higher-order data representations
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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In previouswork [3] we presented 1BC, a first-order Bayesian classifier. 1BC applies dynamic propositionalisation, in the sense that attributes representing first-order features are generated exhaustively within a given feature bias, but during learning rather than as a pre-processing step. In this paper we describe 1BC2, which learns from structured data by fitting various parametric distributions over sets and lists to the data. We evaluate the feasibility of the approach by various experiments.