A support vector machine approach to decision trees
Bennett, K.P.
Blue, J.A.
Rensselaer Polytech. Inst., Troy, NY;
This paper appears in: Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Publication Date: 4-9 May 1998
Volume: 3,
On page(s): 2396-2401 vol.3
Meeting Date: 05/04/1998 - 05/09/1998
Location: Anchorage, AK, USA
ISBN: 0-7803-4859-1
References Cited: 10
INSPEC Accession Number: 6047153
DOI: 10.1109/IJCNN.1998.687237
Posted online: 2002-08-06 21:45:42.0
Abstract
Key ideas from statistical learning theory and support vector
machines are generalized to decision trees. A support vector machine is
used for each decision in the tree. The “optimal” decision
tree is characterized, and both a primal and dual space formulation for
constructing the tree are proposed. The result is a method for
generating logically simple decision trees with multivariate linear,
nonlinear or linear decisions. By varying the kernel function used, the
decisions may consist of linear threshold units, polynomials, sigmoidal
neural networks, or radial basis function networks. The preliminary
results indicate that the method produces simple trees that generalize
well with respect to other decision tree algorithms and single support
vector machines
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