Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
Rule-extraction by backpropagation of polyhedra
Neural Networks
Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Extracting regression rules from neural networks
Neural Networks
A Statistics Based Approach for Extracting Priority Rules from Trained Neural Networks
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
An explanation of reasoning neural networks
Mathematical and Computer Modelling: An International Journal
IEEE Transactions on Neural Networks
Extraction of rules from artificial neural networks for nonlinear regression
IEEE Transactions on Neural Networks
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This study explores the knowledge-internalization process within which a neural-network practitioner embody the explicit knowledge obtained from extracting network's preimage, the set of input values for a given output value, into his/her tacit knowledge. With a number of well-trained single-hidden layer feed-forward neural networks, the practitioner first extracts the (nonlinear) preimage of each trained network. The practitioner then internalizes the explicit outcomes and the insights obtained from the preimage extracting process into his/her tacit knowledge bases. We use the experiment of bond-pricing analysis to illustrate the knowledge-internalization process. This study adds to the literature by introducing the knowledge-internalization process. Moreover, in contrast to the data analyses in previous studies, this study uses mathematical analyses to identify networks' preimages.