Principles of artificial intelligence
Principles of artificial intelligence
Experimental comparison of human and machine learning formalisms
Proceedings of the sixth international workshop on Machine learning
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
C4.5: programs for machine learning
C4.5: programs for machine learning
FOSSIL: a robust relational learner
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Representing biases for inductive logic programming
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Knowledge-based artificial neural networks
Artificial Intelligence
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Inductive Logic Programming: From Machine Learning to Software Engineering
Inductive Logic Programming: From Machine Learning to Software Engineering
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
FONN: Combining First Order Logic with Connectionist Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Applying ILP to Diterpene Structure Elucidation from 13C NMR Spectra
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Multiple Predicate Learning with RTL
AI*IA '95 Proceedings of the 4th Congress of the Italian Association for Artificial Intelligence on Topics in Artificial Intelligence
First-Order Logical Neural Networks
International Journal of Hybrid Intelligent Systems - Recent developments in Hybrid Intelligent Systems
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Learning first-order Bayesian networks
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Upgrading ILP rules to first-order Bayesian networks
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
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This paper presents a method for approximate match of first-order rules with unseen data. The method is useful especially in case of a multi-class problem or a noisy domain where unseen data are often not covered by the rules. Our method employs the Backpropagation Neural Network for the approximation. To build the network, we propose a technique for generating features from the rules to be used as inputs to the network. Our method has been evaluated on four domains of first-order learning problems. The experimental results show improvements of our method over the use of the original rules. We also applied our method to approximate match of propositional rules converted from an unpruned decision tree. In this case, our method can be thought of as soft-pruning of the decision tree. The results on multi-class learning domains in the UCI repository of machine learning databases show that our method performs better than standard C4.5's pruned and unpruned trees.