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A new individual tree-based algorithm for determining forest biomass using small footprint LiDAR data was developed and tested.
The single tree-based algorithm delivered highly reliable estimates for a set of forest structural attributes that are of interest in forest inventories at the landscape scale.
The proposed scheme is a Tree-based Algorithm which Splits torus Networks into two Equally Meshes, hence it is called TASNEM.
The application of a tree-based algorithm on the embryo data would help to more precisely define the effect of patient age and embryo quality (good-quality or poor-quality) on live birth outcome, and to better understand any interactions between these two clinical variables (patient age and embryo quality).
Our contribution is twofold: (i) taking as basis the tree-based algorithm LRT described in [1], we design weaker tree-based models which can be learnt more efficiently; and (ii) we show that bagging these weak learners improves not only the LRT algorithm, but also the state-of-the-art one (IBLR [1]).
In this section, we compare HBA against the tree-based algorithm [7] for always-on networks.
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van der Maaten, L. Accelerating t-SNE using tree-based algorithms.
Maaten, L. V. D. Accelerating t-SNE using tree-based algorithms.
In recent years, tree-based algorithms for mining uncertain data have been developed.
A number of different alternatives were considered for the distance-calculating algorithm: breadth-first search, depth-first search, and a variety of logarithmic tree-based algorithms.
The best individual performance has been observed by the stochastic gradient boosting model followed by Cubist, random forest and model averaged neural networks which except for the latter are all regression tree-based algorithms.
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