Programming in Prolog
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Induction of first-order decision lists: results on learning the past tense of English verbs
Journal of Artificial Intelligence Research
On the Stability of Example-Driven Learning Systems: A Case Study in Multirelational Learning
MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Protocols from perceptual observations
Artificial Intelligence - Special volume on connecting language to the world
Efficient and Scalable Induction of Logic Programs Using a Deductive Database System
Inductive Logic Programming
Parallel ILP for distributed-memory architectures
Machine Learning
Protocols from perceptual observations
Artificial Intelligence - Special volume on connecting language to the world
A behavioral comparison of some probabilistic logic models
Probabilistic inductive logic programming
First-Order rule mining by using graphs created from temporal medical data
AM'03 Proceedings of the Second international conference on Active Mining
Strategies to parallelize ILP systems
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Predicate selection for structural decision trees
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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This chapter describes the theory and use of CPROGOL4.4, a state-of-the-art Inductive Logic Programming (ILP) system. After explaining how to download the source code, the reader is guided through the development of PROGOL input files containing type definitions, mode declarations, back-ground knowledge, examples and integrity constraints. The theory behind the system is then described using a simple example as illustration. The main algorithms in PROGOL are given and methods of pruning the search space of possible hypotheses are discussed. Next the application of built-in procedures for estimating predictive accuracy and statistical significance of PROGOL hypotheses is demonstrated. Lastly, the reader is shown how to use the more advanced features of CPROGOL4.4, including positive-only learning and the use of metalogical predicates for pruning the search space.