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From: jair-ed@ptolemy.arc.nasa.gov
Subject: New Article, Learning Membership Functions in ...
Message-ID: <1995Oct23.215623.12789@ptolemy-ethernet.arc.nasa.gov>
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Date: Mon, 23 Oct 1995 21:56:23 GMT
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JAIR is pleased to announce the publication of the following article:

Woods, K., Cook, D., Hall, L., Bowyer, K. and Stark, L. (1995)
  "Learning Membership Functions in a Function-Based Object Recognition 
   System", Volume 3, pages 187-222.
   Http://seraphim.csee.usf.edu/omlet/omlet-JAIR.html
   PostScript: volume3/woods95a.ps (1.4M)
	       compressed, volume3/woods95a.ps.Z (463K)


   Abstract: Functionality-based recognition systems recognize objects at
   the category level by reasoning about how well the objects support the
   expected function. Such systems naturally associate a ``measure of
   goodness'' or ``membership value'' with a recognized object.  This
   measure of goodness is the result of combining individual measures, or
   membership values, from potentially many primitive evaluations of
   different properties of the object's shape. A membership function is
   used to compute the membership value when evaluating a primitive of a
   particular physical property of an object.  In previous versions of a
   recognition system known as Gruff, the membership function for each of
   the primitive evaluations was hand-crafted by the system designer.  In
   this paper, we provide a learning component for the Gruff system,
   called Omlet, that automatically learns membership functions given a
   set of example objects labeled with their desired category measure.
   The learning algorithm is generally applicable to any problem in which
   low-level membership values are combined through an and-or tree
   structure to give a final overall membership value.

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