Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
C4.5: programs for machine learning
C4.5: programs for machine learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
An Integrated Framework for Empirical Discovery
Machine Learning
Discovery of Relevant Weights by Minimizing Cross-Validation Error
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Computational Characteristics of Law Discovery Using Neural Networks
DS '98 Proceedings of the First International Conference on Discovery Science
Second-Order Learning Algorithm with Squared Penalty Term
Neural Computation
Law discovery using neural networks
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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 proposes a new method for finding polynomials to fit multivariate data containing numeric and nominal variables. Each polynomial is accompanied with the corresponding nominal condition stating when to apply the polynomial. Such a nominally conditioned polynomial is called a rule. A set of such rules can be regarded as a single numeric function, and such a function can be closely approximated by a single three-layer neural network. After training single neural networks with different numbers of hidden units, the method selects the best trained network, and restores the final rules from it. Experiments using three data sets show that the proposed method works well in finding very succinct and interesting rules, even fromda ta containing irrelevant variables and a small amount of noise.