Fuzzy Production Rules: A Learning Methodology

L. Lesmo, L. Saitta and P. Torasso
Additional contact information
L. Lesmo: Università di Torino, Istituto di Scienze dell’Informazione
L. Saitta: Università di Torino, Istituto di Scienze dell’Informazione
P. Torasso: Università di Torino, Istituto di Scienze dell’Informazione

A chapter in Advances in Fuzzy Sets, Possibility Theory, and Applications, 1983, pp 181-198 from Springer

Abstract: 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.

Keywords: Learning Algorithm; Linguistic Variable; Condition Part; Production Rule; Linguistic Term (search for similar items in EconPapers)
Date: 1983
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4613-3754-6_13

Ordering information: This item can be ordered from
http://www.springer.com/9781461337546

DOI: 10.1007/978-1-4613-3754-6_13

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().