In article <1br8uygr4n.fsf@cs.nmsu.edu>, Joe Pfeiffer writes:> I'm currently reading the book mentioned above; I'm wondering about > something... > > He attempts to define the membership function of a set by using > what he calls ``calibrational propositions'' -- the idea is that if > you ask 70 people if John is tall, and 70 of them say ``yes,'' then > mu(tall) = .7. While this seems to do a good job of capturing common > word usage, it's not at all clear to me that it captures the fuzzy > behavior of the ``tall'' set; it seems probabilistic rather than > fuzzy. > > So, what are other people's reactions?
Same for me. I haven't read the book, but this `voting approach' is often used as a justification for membership degrees. My standard counterexample is an orange. While nobody in their right mind would say that an orange is `red', I bet a lot of people would agree it's at least `somewhat red'. So while the probability that an orange would be called `red' might be zero, I'd give it a non-zero membership degree in the in the fuzzy set of red objects. For empirically finding a membership degree, I'd rather have people mark the `degree of tallness' on a continuous scale between `tall' and `not tall at all' and take the average. See also the paper @ARTICLE{Zimmermann/Zysno80, language = "USenglish", author = "H.-J. Zimmermann and P. Zysno", title = "Latent connectives in human decision making", journal = FSS, year = 1980, volume = 4, pages = "37-51"} regards Stephan -- Stephan Lehmke Stephan.Lehmke@cs.uni-dortmund.de Fachbereich Informatik, LS I Tel. +49 231 755 6434 Universitaet Dortmund FAX 6555 D-44221 Dortmund, Germany