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
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Discovering Empirical Laws of Web Dynamics
SAINT '02 Proceedings of the 2002 Symposium on Applications and the Internet
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
Discovery of a Set of Nominally Conditioned Polynomials
DS '99 Proceedings of the Second International Conference on Discovery Science
DS '00 Proceedings of the Third International Conference on Discovery Science
Computational Revision of Quantitative Scientific Models
DS '01 Proceedings of the 4th International Conference on Discovery Science
Finding Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
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
Structuring Neural Networks through Bidirectional Clustering of Weights
DS '02 Proceedings of the 5th International Conference on Discovery Science
Fast and Stable Learning Utilizing Singular Regions of Multilayer Perceptron
Neural Processing Letters
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This paper proposes an improved version of a method for discovering 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 approximated and learned by three-layer neural networks. The method selects the best from those trained neural networks with different numbers of hidden units by a newly introduced double layer of cross-validation, and restores the final rules from the best. Experiments using two data sets show that the proposed method works well in discovering very succinct and interesting rules even from data containing irrelevant variables and a small amount of noise.