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Toplam kayıt 5, listelenen: 1-5
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 ...
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 ...
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/?-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) ...
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 ...