Combining Symbolic and Neural Learning
Machine Learning
Symbolic Representation of Neural Networks
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Knowledge-based neurocomputing
Knowledge-based neurocomputing
Extraction of linguistic rules from data via neural networks and fuzzy approximation
Knowledge-based neurocomputing
A competitive elliptical clustering algorithm
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
A connectionist approach to generating oblique decision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
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This paper proposes a knowledge-based neurocomputing approach to extract and refine a set of linguistic rules from data. A neural network is designed along with its learning algorithm that allows simultaneous definition of the structure and the parameters of the rule base. The network can be regarded both as an adaptive rule-based system with the capability of learning fuzzy rules from data, and as a connectionist architecture provided with linguistic meaning. Experimental results on two well-known classification problems illustrate the effectiveness of the proposed approach.