New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Machine Learning
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Classification of Individuals with Complex Structure
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Higher-Order Computational Logic
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part I
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Using ILP to construct features for information extraction from semi-structured text
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Application of decisional DNA in web data mining
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
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This tutorial discusses some knowledge representation issues in machine learning. The focus is on machine learning applications for which the individuals that are the subject of learning have complex structure. To represent such individuals, a rich knowledge representation language based on higher-order logic is introduced. The logic is also employed to construct comprehensible hypotheses that one might want to learn about the individuals. The tutorial introduces the main ideas of this approach to knowledge representation in a mostly informal way and gives a number of illustrations. The application of the ideas to decision-tree learning is also illustrated with an example.