Boolean Feature Discovery in Empirical Learning
Machine Learning
A general framework for parallel distributed processing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The Utility of Knowledge in Inductive Learning
Machine Learning
Compiling prior knowledge into an explicit basis
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Knowledge-based artificial neural networks
Artificial Intelligence
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Effective and Efficient Knowledge Base Refinement
Machine Learning
Representation Operators and Computation
Minds and Machines
Maximizing Theory Accuracy Through Selective Reinterpretation
Machine Learning
Extracting Information from the Web for Concept Learning and Collaborative Filtering
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Data-Driven Theory Refinement Using KBDistAl
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Domain knowledge to support the discovery process: previously discovered knowledge
Handbook of data mining and knowledge discovery
Information architecture without internal theory: an inductive design process
Journal of the American Society for Information Science and Technology
The Knowledge Engineering Review
Connectionist theory refinement: genetically searching the space of network topologies
Journal of Artificial Intelligence Research
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Theory revision integrates inductive learning and background knowledge by combining training examples with a coarse domain theory to produce a more accurate theory. There are two challenges that theory revision and other theory-guided systems face. First, a representation language appropriate for the initial theory may be inappropriate for an improved theory. While the original representation may concisely express the initial theory, a more accurate theory forced to use that same representation may be bulky, cumbersome, and difficult to reach. Second, a theory structure suitable for a coarse domain theory may be insufficient for a fine-tuned theory. Systems that produce only small, local changes to a theory have limited value for accomplishing complex structural alterations that may be required. Consequently, advanced theory-guided learning systems require flexible representation and flexible structure. An analysis of various theory revision systems and theory-guided learning systems reveals specific strengths and weaknesses in terms of these two desired properties. Designed to capture the underlying qualities of each system, a new system uses theory-guided constructive induction. Experiments in three domains show improvement over previous theory-guided systems. This leads to a study of the behavior, limitations, and potential of theory-guided constructive induction.