Ara
Toplam kayıt 6, listelenen: 1-6
Calculating the VC-dimension of decision trees
(IEEE, 2009)
We propose an exhaustive search algorithm that calculates the VC-dimension of univariate decision trees with binary features. The VC-dimension of the univariate decision tree with binary features depends on (i) the ...
Regularizing soft decision trees
(Springer, 2013)
Recently, we have proposed a new decision tree family called soft decision trees where a node chooses both its left and right children with different probabilities as given by a gating function, different from a hard ...
Budding trees
(IEEE Computer Soc, 2014-08-24)
We propose a new decision tree model, named the budding tree, where a node can be both a leaf and an internal decision node. Each bud node starts as a leaf node, can then grow children, but then later on, if necessary, its ...
Soft decision trees
(IEEE, 2012)
We discuss a novel decision tree architecture with soft decisions at the internal nodes where we choose both children with probabilities given by a sigmoid gating function. Our algorithm is incremental where new nodes are ...
Multivariate statistical tests for comparing classification algorithms
(Springer, Berlin, Heidelberg, 2011)
The misclassification error which is usually used in tests to compare classification algorithms, does not make a distinction between the sources of error, namely, false positives and false negatives. Instead of summing ...
Statistical tests using hinge/ε-sensitive loss
(Springer-Verlag, 2013)
Statistical tests used in the literature to compare algorithms use the misclassification error which is based on the 0/1 loss and square loss for regression. Kernel-based, support vector machine classifiers (regressors) ...