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Knowledge discovery by inductive neural networks

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1 Author(s)
Limin Fu ; Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL, USA

A new neural network model for inducing symbolic knowledge from empirical data is presented. This model capitalizes on the fact that the certainty factor-based activation function can improve the network generalization performance from a limited amount of training data. The formal properties of the procedure for extracting symbolic knowledge from such a trained neural network are investigated. In the domain of molecular genetics, a case study demonstrated that the described learning system effectively discovered the prior domain knowledge with some degree of refinement. Also, in cross-validation experiments, the system outperformed C4.5, a commonly used rule learning system

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:11 ,  Issue: 6 )

Date of Publication:

Nov/Dec 1999

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