Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
The Utility of Knowledge in Inductive Learning
Machine Learning
Efficient top-down induction of logic programs
ACM SIGART Bulletin
Flattening and Saturation: Two Representation Changes for Generalization
Machine Learning - Special issue on evaluating and changing representation
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Applications of machine learning and rule induction
Communications of the ACM
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
PROLOG Programming for Artificial Intelligence
PROLOG Programming for Artificial Intelligence
Learning Logical Definitions from Relations
Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Induction of first-order decision lists: results on learning the past tense of English verbs
Journal of Artificial Intelligence Research
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
An introduction to inductive logic programming and learning language in logic
Learning language in logic
How to upgrade propositional learners to first order logic: case study
Relational Data Mining
Relational learning and boosting
Relational Data Mining
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
How to Upgrade Propositional Learners to First Order Logic: A Case Study
Machine Learning and Its Applications, Advanced Lectures
Experiments in Predicting Biodegradability
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Data & Knowledge Engineering
Analysis and Evaluation of Inductive Programming Systems in a Higher-Order Framework
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Tuning up FOIL for extracting information from the web
International Journal of Computer Applications in Technology
An introduction to inductive programming
Artificial Intelligence Review
Learning source descriptions for web services
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Top-down induction of first-order logical decision trees
Artificial Intelligence
I/O guided detection of list catamorphisms: towards problem specific use of program templates in IP
Proceedings of the 2010 ACM SIGPLAN workshop on Partial evaluation and program manipulation
Efficient and effective induction of first order decision lists
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Automatic induction of bellman-error features for probabilistic planning
Journal of Artificial Intelligence Research
Towards learning to detect meaningful changes in software
Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering
Distributed classification of textual documents on the grid
HPCC'06 Proceedings of the Second international conference on High Performance Computing and Communications
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First-order learning involves finding a clause-form definition of a relation from examples of the relation and relevant background information. In this paper, a particular first-order learning system is modified to customize it for finding definitions of functional relations. This restriction leads to faster learning times and, in some cases, to definitions that have higher predictive accuracy. Other first-order learning systems might benefit from similar specialization.