Using explanation-based and empirical methods in theory revision
Using explanation-based and empirical methods in theory revision
Machine Learning - Special issue on multistrategy learning
Machine Learning
An anytime approach to connectionist theory refinement: refining the topologies of knowledge-based neural networks
Rule Revision With Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Data-Driven Constructive Induction
IEEE Intelligent Systems
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Feature Space Transformation Using Genetic Algorithms
IEEE Intelligent Systems
Machine Learning
Efficient Feature Selection in Conceptual Clustering
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
FONN: Combining First Order Logic with Connectionist Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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Advances in Neural Information Processing Systems 5, [NIPS Conference]
Combining symbolic and connectionist learning methods to refine certainty-factor rule-bases
Combining symbolic and connectionist learning methods to refine certainty-factor rule-bases
Refining Numerical Constants in First Order Logic Theories
Machine Learning - Special issue on multistrategy learning
Integrated Architectures for Machine Learning
Machine Learning and Its Applications, Advanced Lectures
Process-Specific Information for Learning Electronic Negotiation Outcomes
Fundamenta Informaticae
Process-Specific Information for Learning Electronic Negotiation Outcomes
Fundamenta Informaticae
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Expert classification systems have proven themselves effectivedecision makers for many types of problems. However, the accuracy of suchsystems is often highly dependent upon the accuracy of a human expert'sdomain theory. When human experts learn or create a set of rules, they aresubject to a number of hindrances. Most significantly experts are, to agreater or lesser extent, restricted by the tradition of scholarship which haspreceded them and by an inability to examine large amounts of data ina rigorous fashion without the effects of boredom or frustration. Asa result, human theories are often erroneous or incomplete. To escapethis dependency, machine learning systems have been developedto automatically refine and correct an expert's domain theory.When theory revision systems are applied to expert theories, they oftenconcentrate on the reformulation of the knowledge provided rather than onthe reformulation or selection of input features. The general assumptionseems to be that the expert has already selected the set of features thatwill be most useful for the given task. That set may, however, be suboptimal.This paper studies theory refinement and the relative benefits of applyingfeature selection versus more extensive theory reformulation.