Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
C4.5: programs for machine learning
C4.5: programs for machine learning
Transferring previously learned back-propagation neural networks to new learning tasks
Transferring previously learned back-propagation neural networks to new learning tasks
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Learning to recognize promoter sequences in E. coli by modeling uncertainty in the training data
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
An event set approach to sequence discovery in medical data
Intelligent Data Analysis
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Decision Support Systems
Learning strategies for task delegation in norm-governed environments
Autonomous Agents and Multi-Agent Systems
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This work uses background knowledge to reexpress training data in a form more appropriate for inductive learning. The approach dramatically improves the results of decision-tree and neural network learning methods.