Now showing items 1-3 of 3
VC-Dimension of Univariate Decision Trees
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2015-02)
In this paper, we give and prove the lower bounds of the Vapnik-Chervonenkis (VC)-dimension of the univariate decision tree hypothesis class. The VC-dimension of the univariate decision tree depends on the VC-dimension ...
Model selection in omnivariate decision trees using Structural Risk Minimization
(ELSEVIER SCIENCE INC, 2011-12)
As opposed to trees that use a single type of decision node, an omnivariate decision tree contains nodes of different types. We propose to use Structural Risk Minimization (SRM) to choose between node types in omnivariate ...
Cost-conscious comparison of supervised learning algorithms over multiple data sets
(ELSEVIER SCI LTD, 2012-04)
In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi(2)Test, a generalization ...