Advantages of decision lists and implicit negatives in inductive logic programming
New Generation Computing
Relational learning of pattern-match rules for information extraction
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Machine Learning
Learning Multilingual Morphology with CLOG
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
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Top-down induction of first-order logical decision trees
Artificial Intelligence
Introducing possibilistic logic in ILP for dealing with exceptions
Artificial Intelligence
Possibilistic inductive logic programming
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Hi-index | 0.00 |
We present BUFOIDL, a new bottom-up algorithm for learning first order decision lists. Although first order decision lists have potential as a representation for learning concepts that include exceptions, such as language constructs, previous systems suffered from limitations that we seek to overcome in BUFOIDL. We present experiments comparing BUFOIDL to previous work in the area, demonstrating the system's potential.