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 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.
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