Alternating decision trees introduce structure to the set of hypotheses by requiring that they build off a hypothesis that was produced in an earlier iteration. The resulting set of hypotheses can be visualized in a tree based on the relationship between a hypothesis and its "parent."
This paper introduces a novel classification method termed Alternating Decision Forests (ADFs), which formulates the training of Random Forests explicitly ...
This paper introduces a novel classification method termed Alternating Decision Forests (ADFs), which formu- lates the training of Random Forests explicitly ...
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This paper introduces a novel classification method termed Alternating Decision Forests (ADFs), which formu- lates the training of Random Forests explicitly ...
Alternating Decision Tree (ADTree) is a special class of classification models. It is a generalization of classical Decision Trees, Voted Decision Trees, ...
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This paper introduces a novel classification method termed Alternating Decision Forests (ADFs), which formulates the training of Random Forests explicitly ...
This paper introduces a novel classification method termed Alternating Decision Forests (ADFs), which formulates the training of Random Forests explicitly ...
We present a learning algorithm for alternat- ing decision trees that is based on boosting. Experimental results show it is competitive with boosted decision ...
Missing: Forests. | Show results with:Forests.
The alternating decision tree (ADTree) is a successful clas- sification technique that combines decision trees with the predictive ac- curacy of boosting into a ...
Sep 19, 2023 · This page collects papers describing the "tree alternating optimisation" (TAO) algorithm, a new way to train many types of decision trees ...