Learning Control Systems-Review and Outlook
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special memorial issue for Professor King-Sun Fu
Some results on the complexity of knowledge-base refinement
Proceedings of the sixth international workshop on Machine learning
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Use of meta level knowledge in the construction and maintenance of large knowledge bases.
Use of meta level knowledge in the construction and maintenance of large knowledge bases.
Learning object-level and meta-level knowledge in expert systems (machine learning)
Learning object-level and meta-level knowledge in expert systems (machine learning)
Structuring Knowledge In Vague Domains
IEEE Transactions on Knowledge and Data Engineering
Direct transfer of learned information among neural networks
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
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A novel technique that applies the neural-network learning strategy of back-propagation to recognize semantically incorrect rules is presented. When the rule strengths of most rules are semantically correct, semantically incorrect rules can be recognized if their strengths are weakened or change signs after training with correct samples. In each training cycle, the discrepancies in the belief values of goal hypotheses are propagated backward and the strengths of rules responsible for such discrepancies are modified appropriately. A function called consistent-shift is defined for measuring the shift of a rule strength in the direction consistent with the strength assigned before training and is a critical component of this technique. The viability of this technique has been demonstrated in a practical domain.