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Tree Ensembles on the induced discrete space
(Institute of Electrical and Electronics Engineers Inc., 2016-05)
Decision trees are widely used predictive models in machine learning. Recently, K-tree is proposed, where the original discrete feature space is expanded by generating all orderings of values of k discrete attributes and ...
Constructing a Turkish constituency parse treeBank
(Springer Verlag, 2016)
In this paper, we describe our initial efforts for creating a Turkish constituency parse treebank by utilizing the English Penn Treebank. We employ a semiautomated approach for annotation. In our previouswork [18], the ...
English-Turkish parallel treebank with morphological annotations and its use in tree-based SMT
(SciTePress, 2016)
In this paper, we report our tree based statistical translation study from English to Turkish. We describe our data generation process and report the initial results of tree-based translation under a simple model. For ...
Bagging soft decision trees
(Springer Verlag, 2016)
The decision tree is one of the earliest predictive models in machine learning. In the soft decision tree, based on the hierarchical mixture of experts model, internal binary nodes take soft decisions and choose both ...
A novel kernel to predict software defectiveness
(Elsevier Science Inc, 2016-09)
Although the software defect prediction problem has been researched for a long time, the results achieved are not so bright. In this paper, we propose to use novel kernels for defect prediction that are based on the ...
Incremental construction of rule ensembles using classifiers produced by different class orderings
(IEEE, 2016)
In this paper, we discuss a novel approach to incrementally construct a rule ensemble. The approach constructs an ensemble from a dynamically generated set of rule classifiers. Each classifier in this set is trained by ...