Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Instance-based prediction of real-valued attributes
Computational Intelligence
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
An Integrated Framework for Empirical Discovery
Machine Learning
Computational Characteristics of Law Discovery Using Neural Networks
DS '98 Proceedings of the First International Conference on Discovery Science
Law discovery using neural networks
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
DS '00 Proceedings of the Third International Conference on Discovery Science
Discovering Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
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This paper shows that a connectionist law discovery method called RF6 can discover a law in the form of a set of nominally conditioned polynomials, from data containing both nominal and numeric values. RF6 learns a compound of nominally conditioned polynomials by using single neural networks, and selects the best one among candidate networks, and decomposes the selected network into a set of rules. Here a rule means a nominally conditioned polynomial. Experiments showed that the proposed method works well in discovering such a law even from data containing irrelevant variables and a small amount of noise.