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
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
Data mining tasks and methods: Equation fitting: equation finders
Handbook of data mining and knowledge discovery
Automated scientific discovery
Handbook of data mining and knowledge discovery
Law discovery using neural networks
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
DS'10 Proceedings of the 13th international conference on Discovery science
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Automated Discovery Of Empirical Laws
Fundamenta Informaticae
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This article reports the results of a study of domain-independent function finding based on a collection of several hundred real scientific data sets. Prior studies have introduced the controversial idea of discovering functional relatonships of interest to scientists directly from the data they collect. The evidence presented here supports the view that this is sometimes possible, but it also suggests how often purely data-driven discovery is not possible and how much more difficult it may be than has often been assumed. Experience with sampled examples of real scientific data suggests as well that emphasis on search in prior studies may have been misplaced. For the function-finding problems studied here, scientists typically propose only a handful of different functional relationships. The difficulty is not in searching through a large space of relationships but in evaluating a few common ones to determine if they are likely to be of scientific interest.