Abstract
Empirical induction of numeric laws aims at finding numerical laws from an experimentation set, using symbolic and inductive techniques. Faced with experimental data, these techniques must be adapted in order to deal with noisy data. In this article, we present the solutions for dealing with numerical uncertainty which are implemented in our system A R C [16]. These solutions are inspired by experimental science and join simplicity to efficiency. Moreover, when used in a more realistic way, numerical uncertainty can significantly improve not only the accuracy of the results but also the search algorithm's efficiency.
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Moulet, M. (1995). The role of measurement uncertainty in numeric law induction. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Advances in Intelligent Computing — IPMU '94. IPMU 1994. Lecture Notes in Computer Science, vol 945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035995
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DOI: https://doi.org/10.1007/BFb0035995
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