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Fuzzy Quantifiers - A Computational Theory (Hardcover, 2006 ed.)
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Fuzzy Quantifiers - A Computational Theory (Hardcover, 2006 ed.)
Series: Studies in Fuzziness and Soft Computing, 193
Expected to ship within 12 - 17 working days
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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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