Stephan.Lehmke@cs.uni-dortmund.de (Stephan Lehmke) writes:>> >> 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 Of course, I meant ``ask 100 people'' as somebody else pointed out. The keyboard gremlins got in...>> 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?
<snip>>> 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.
Thanks. I'm coming into this from other fields, and wanted to make sure I wasn't misreading, and that others had the same queasy feeling I did.> 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"}
Sorry, I'm not familiar with the journal. What's FSS? Thanks, -- Joseph J. Pfeiffer, Jr., Ph.D. Phone -- (505) 646-1605 Department of Computer Science FAX -- (505) 646-1002 New Mexico State University http://www.cs.nmsu.edu/~pfeiffer SWNMRSEF: http://www.nmsu.edu/~scifair