Toward combining empirical and analytical methods for inferring heuristics
Proc. of the international NATO symposium on Artificial and human intelligence
Error correction in constructive induction
Proceedings of the sixth international workshop on Machine learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
A General Framework for Induction and a Study of Selective Induction
Machine Learning
Version spaces: an approach to concept learning.
Version spaces: an approach to concept learning.
Taxonomic Conversational Case-Based Reasoning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Learning from textbook knowledge: a case study
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Learning from an approximate theory and noisy examples
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
On the learnability of abstraction theories from observations for relational learning
ECML'05 Proceedings of the 16th European conference on Machine Learning
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We report on a learning system MIRO which performs supervised concept formation in an abstraction space. Given a domain theory, the method constructs this abstraction space by deduction over instances, and then performs induction in it rather than the initial space defined by instances alone. It is also possible to regard MIRO as a variant of constructive induction. The Vapnik-Chervonenkis model suggests that learning in an abstraction space can result in a substantial speedup, and we provide empirical studies which validate this proposition. We also show that learning in an abstraction space can reduce the number of false negative and false postive classifications because coincidental patterns are filtered by the deduction process. The method is able to extend an incomplete domain theory represented as at tribute-value pairs with a set of rules that represent a disjunctive concept derived from a batch of training instances.