Steps toward artificial intelligence
Computers & thought
Learning Structural Descriptions From Examples
Learning Structural Descriptions From Examples
Active learning for hierarchical wrapper induction
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
Emerging Patterns and Classification
ASIAN '00 Proceedings of the 6th Asian Computing Science Conference on Advances in Computing Science
Learning Patterns in Multidimensional Space Using Interval Algebra
AIMSA '02 Proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
A Method for Predicting Solutions in Case-Based Problem Solving
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Preference elicitation with subjective features
Proceedings of the third ACM conference on Recommender systems
On the Foundations of Noise-free Selective Classification
The Journal of Machine Learning Research
A taxonomic generalization technique for natural language processing
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Learning complex concepts using crowdsourcing: a Bayesian approach
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Generality is predictive of prediction accuracy
Data Mining
Active learning via perfect selective classification
The Journal of Machine Learning Research
ON SETS OF TERMS: A STUDY OF A GENERALISATION RELATION AND OF ITS ALGORITHMIC PROPERTIES
Fundamenta Informaticae
Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty
Information Sciences: an International Journal
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An important research problem in artificial intelligence is the study of methods for learning general concepts or rules from a set of training instances. An approach to this problem is presented which is guaranteed to find, without backtracing, all rule versions consistent with a set of positive and negative training instances. The algorithm put forth uses a representation of the space of those rules consistent with the observed training data. This "rule version space" is modified in response to new training instances by eliminating candidate rule versions found to conflict with each new instance. The use of version spaces is discussed in the context of Meta-DENDRAL, a program which learns rules in the domain of chemical spectroscopy.