The Utility of Knowledge in Inductive Learning
Machine Learning
Knowledge-based artificial neural networks
Artificial Intelligence
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Algorithmic Program DeBugging
Feature Subset Selection and Inductive Logic Programming
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning concepts by performing experiments (marvin)
Learning concepts by performing experiments (marvin)
An empirical study of the use of relevance information in inductive logic programming
The Journal of Machine Learning Research
Knowledge-Based Kernel Approximation
The Journal of Machine Learning Research
Argument based machine learning
Artificial Intelligence
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Duce, an oracle-based approach to constructive induction
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Integrating knowledge capture and supervised learning through a human-computer interface
Proceedings of the sixth international conference on Knowledge capture
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Inductive Logic Programming (ILP) provides an effective method of learning logical theories given a set of positive examples, a set of negative examples, a corpus of background knowledge, and specification of a search space (e.g., via mode definitions) from which to compose the theories. While specifying positive and negative examples is relatively straightforward, composing effective background knowledge and search-space definition requires detailed understanding of many aspects of the ILP process and limits the usability of ILP. We introduce two techniques to automate the use of ILP for a non-ILP expert. These techniques include automatic generation of background knowledge from user-supplied information in the form of a simple relevance language, used to describe important aspects of specific training examples, and an iterative-deepening-style search process.