Geometry-Based Learning Algorithms
			    George H. John
		     Computer Science Department
			 Stanford University
			  Stanford, CA 94305
			gjohn@cs.Stanford.EDU

			      March 1993

      UNPUBLISHED DRAFT -- NOT FOR CITATION -- COMMENTS WELCOME
  
  We present CHILS, the Convex Hull Inductive Learning System, a novel
  supervised learning algorithm based on approximating concepts with
  sets of convex hulls.  We introduce a theoretical methodology for
  describing the power of a concept representation language and use it
  to compare convex hulls with other geometrical concept
  representations.  The Domain Transform framework (DT) provides a clear
  way to compare the power of supervised learning systems, allowing us
  to characterize a class of domains which is learnable by some systems
  but cannot be learned by other systems.  DT can be used similarly to
  compare the expected generalization performance of different domains.