Abstract
In many research fields it is possible to obtain good scientific results only after large amounts of data have been collected and analyzed; the analysis allows the researcher to detect regularities, similarities and discriminant features which may be useful to characterize different classes of objects. On the other hand, the manual examination of a large set of data is slow and error prone, so that many techniques have been proposed and are actually used to perform that analysis automatically (e.g. discriminant analysis); unfortunately, most of those techniques are based on mathematical methodologies which impose strong constraints on the kinds of data that can be analyzed.
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© 1983 Plenum Press, New York
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Lesmo, L., Saitta, L., Torasso, P. (1983). Fuzzy Production Rules: A Learning Methodology. In: Wang, P.P. (eds) Advances in Fuzzy Sets, Possibility Theory, and Applications. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-3754-6_13
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DOI: https://doi.org/10.1007/978-1-4613-3754-6_13
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