Experimental comparison of human and machine learning formalisms
Proceedings of the sixth international workshop on Machine learning
Proceedings of the sixth international workshop on Machine learning
Learning Logical Definitions from Relations
Machine Learning
Learning Conjunctive Concepts in Structural Domains
Machine Learning
Top-down induction of first-order logical decision trees
Artificial Intelligence
Learning from an approximate theory and noisy examples
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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STRUCT is a system that learns structural decision trees from positive and negative examples. The algorithm uses a modification of Pagallo and Haussler's FRINGE algorithm to construct new features in a first-order representation. Experiments compare the effects of different hypothesis evaluation strategies, domain representation, and feature construction. STRUCT is also compared with Quinlan's FOIL on two domains. The results show that a modified FRINGE algorithm improves accuracy, but that it is sensitive to the distribution of the examples.