Communications of the ACM
Explicitly biased generalization
Computational Intelligence
Learning from hints in neural networks
Journal of Complexity
Symbolic-neural systems and the use of hints for developing complex systems
International Journal of Man-Machine Studies
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Neural networks and the bias/variance dilemma
Neural Computation
Combining Symbolic and Neural Learning
Machine Learning
Knowledge-based artificial neural networks
Artificial Intelligence
Evaluation and Selection of Biases in Machine Learning
Machine Learning - Special issue on bias evaluation and selection
Inductive Policy: The Pragmatics of Bias Selection
Machine Learning - Special issue on bias evaluation and selection
Recursive Automatic Bias Selection for Classifier Construction
Machine Learning - Special issue on bias evaluation and selection
Shifting Vocabulary Bias in Speedup Learning
Machine Learning - Special issue on bias evaluation and selection
Extraction of rules from discrete-time recurrent neural networks
Neural Networks
Unified Integration of Explicit Knowledge and Learning by Example in Recurrent Networks
IEEE Transactions on Knowledge and Data Engineering
Rule Revision With Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Explanation-Based Neural Network Learning for Robot Control
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Integration of Neural Networks and Knowledge-Based Systems in Medicine
AIME '95 Proceedings of the 5th Conference on Artificial Intelligence in Medicine in Europe: Artificial Intelligence Medicine
Network Structuring and Training Using Rule-Based Knowledge
Advances in Neural Information Processing Systems 5, [NIPS Conference]
What Inductive Bias Gives Good Neural Network Training Performance?
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
How to shift bias: Lessons from the baldwin effect
Evolutionary Computation
Refinement of approximate domain theories by knowledge-based neural networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Neural computation in medicine
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Learning capacity and sample complexity on expert networks
IEEE Transactions on Neural Networks
Back-propagation learning in expert networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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The integration of symbolic knowledge with artificial neural networks is becoming an increasingly popular paradigm for solving real-world applications. The paradigm provides means for using prior knowledge to determine the network architecture, to program a subset of weights to induce a learning bias which guide network training, and to extract knowledge from trained networks. The role of neural networks then becomes that of knowledge refinement. It thus provides a methodology for dealing with uncertainty in the prior knowledge. We address the open question of how to determine the strength of the inductive bias of programmed weights; we present a quantitative solution which takes the network architecture, the prior knowledge, and the training data into consideration. We apply our solution to the difficult problem of analyzing breast tissue from magnetic resonance spectroscopy (MRS); the available database is extremely limited and cannot be adequately explained by expert knowledge alone.