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Fuzzy Quantifiers - A Computational Theory (Paperback, Softcover reprint of hardcover 1st ed. 2006)
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Fuzzy Quantifiers - A Computational Theory (Paperback, Softcover reprint of hardcover 1st ed. 2006)
Series: Studies in Fuzziness and Soft Computing, 193
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From a linguistic perspective, it is quanti?cation which makes all
the di?- ence between "having no dollars" and "having a lot of
dollars." And it is the meaning of the quanti?er "most" which
eventually decides if "Most Ame- cans voted Kerry" or "Most
Americans voted Bush" (as it stands). Natural
language(NL)quanti?erslike"all,""almostall,""many"etc. serveanimp-
tant purpose because they permit us to speak about properties of
collections, as opposed to describing speci?c individuals only; in
technical terms, qu- ti?ers are a 'second-order' construct. Thus
the quantifying statement "Most Americans voted Bush" asserts that
the set of voters of George W. Bush c-
prisesthemajorityofAmericans,
while"Bushsneezes"onlytellsussomething about a speci?c individual.
By describing collections rather than individuals, quanti?ers
extend the expressive power of natural languages far beyond that of
propositional logic and make them a universal communication medium.
Hence language heavily depends on quantifying constructions. These
often involve fuzzy concepts like "tall," and they frequently refer
to fuzzy quantities in agreement like "about ten," "almost all,"
"many" etc. In order to exploit this expressive power and make
fuzzy quanti?cation available to technical applications, a number
of proposals have been made how to model fuzzy quanti?ers in the
framework of fuzzy set theory. These approaches usually reduce
fuzzy quanti?cation to a comparison of scalar or fuzzy
cardinalities 197, 132].
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