Abstract
In this article we put forward an extention to the hierarchical
CART classification method which uses quadratic decision borders.
The original CART applies univariate splits on individual
variables as well as splits on combinations of variables to
recursively partition the feature-space into subsets which are
successively more class-homogeneous. Guided by the fact that
class-distributions in feature-space are very often
hyper-elliptical shaped, we give an extension to the original CART
which also uses quadratic shaped decision borders which can be
modelled by a mean-vector and a dispersion matrix. We propose a
scheme for finding a good starting guess for a quadratic decision
border, and a scheme for subsequently improving the guess, through
adjustments of the size and shape of the decision border.
Original language | English |
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Title of host publication | Proceedings from The 8th Danish Conference on Pattern Recognition and Image Analysis |
Place of Publication | Copenhagen |
Publisher | DIKU TRYK |
Publication date | 1999 |
Pages | 67-73 |
Publication status | Published - 1999 |
Event | The 8th Danish Conference on Pattern Recognition and Image Analysis - Copenhagen, Denmark Duration: 26 Aug 1999 → 26 Aug 1999 Conference number: 8 |
Conference
Conference | The 8th Danish Conference on Pattern Recognition and Image Analysis |
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Number | 8 |
Country/Territory | Denmark |
City | Copenhagen |
Period | 26/08/1999 → 26/08/1999 |