Finding Multivariate Splits in Decision Trees
		     Using Function Optimization

			     George John
			Computer Science Dept.
			 Stanford University
			  Stanford, CA 94305
			gjohn@cs.stanford.edu


We present a new method for top-down induction of decision trees
(TDIDT) with multivariate binary splits at the nodes.  The primary
contribution of this work is a new splitting criterion called soft
entropy, which is continuous and differentiable with respect to the
parameters of the splitting function.  Using simple gradient descent
to find multivariate splits and a novel pruning technique, our
TDIDT-SEH (Soft Entropy Hyperplanes) algorithm is able to learn very
small trees with better accuracy than competing learning algorithms on
most datasets examined.


Citation: 
John, George (1994) Finding Multivariate Splits in Decision Trees
Using Function Optimization.  Proceedings of the National Conference
on Artificial Intelligence (AAAI-94), p 1463.  AAAI/MIT Press.

Note that the paper is a "Student Abstract," and is only one page
long.