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The second entry in the second row indicates the percentage of units retained when only posterior probability based pruning is applied.
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Furthermore, when pruning is applied, additional and notable resource consumption savings can be achieved without degrading original performance.
To keep computations tractable, the Gaussian component pruning is applied in this numerical example with a pruning threshold τ = 10− 5, a merging threshold U = 20, and a maximum number of Gaussian components J max = 100.
In order to control for overfitting and finding the optimal CART based tree size, minimal cost-complexity pruning was applied.
In order to reduce the size of the resulting index, two stages of pruning were applied.
However, no pruning was applied in the experiments since the model size without any pruning was already much smaller compared to the GMM system.
Up to the point of sampling no management operations such as thinning or pruning were applied to the sampled trees.
The pruning was applied to the diabetes and Crohn's datasets and SNPs were pruned such that they were at least 30 kb apart.
Standard orchard management including fertilisation, irrigation and pruning were applied.
In order to avoid a combinatory explosion, a pruning process is applied to keep the tree at a reasonable size.
Feature selection method is used to select relevant variables, and pruning technique is applied to obtain low information-burden models.
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