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Model selection in omnivariate decision trees using Structural Risk Minimization
(Elsevier Science Inc, 2011-12-01)
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 ...
VC-dimension of univariate decision trees
(IEEE-INST Electrical Electronics Engineers Inc, 2015-02-25)
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 ...
An incremental model selection algorithm based on cross-validation for finding the architecture of a Hidden Markov model on hand gesture data sets
(IEEE, 2009-12-13)
In a multi-parameter learning problem, besides choosing the architecture of the learner, there is the problem of finding the optimal parameters to get maximum performance. When the number of parameters to be tuned increases, ...
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 ...