Sentence examples for incremental merging from inspiring English sources

She proposes an incremental merging and phase out of all travel zones, cutting Outer London fares as she goes while letting Inner London fares rise with inflation until convergence is achieved by 2025.

The incremental merging of acquired topic tree model for more recent times uses a procedure for detecting the attachment point of new model as has been described in [18].

While many engineering tasks can, and should be, manageable independently, it does place a great burden on explicit collaboration needs including the need for frequent and incremental merging of artifacts that software engineers manipulate using these tools.

We now show that incremental merging is possible; that is, we can start by performing the merging based on a subset consisting of sensor nodes, and it can be guaranteed that the merging decisions that were valid when  nodes were considered will remain valid even when all  nodes are taken into account.

Due to the retrieval of coarse resolution modules, whose large sizes depend on incremental merging of small modules, a weakness of the MaxMod approach concerns its possible interpretation in biological applications.

ChiMerge [11], Chi2 [12], Modified Chi2 [13], and area-based [14] are examples of univariate, supervised, incremental and merging methods.

Our analysis allows us to (1) extend the known theory of admissibility of concept spaces for incremental version space merging, and (2) develop new, simpler label-update algorithms for ATMSs with DNF assumption formulas.

This is because the incremental property of the merging technique allows us to find more identifiable bins at each node.

Discretization techniques can be classified in several different ways, such as supervised versus unsupervised, univariate versus multivariate, splitting versus merging, direct versus incremental, and more [6 9].

All major components of SWIFT (weighted iterative sampling, the incremental EM iterations, and efficient LDA-based merging) are designed to be efficiently scalable to big datasets, providing a significant improvement over the existing soft clustering methods 9– 14, 14.

To allow for incremental training data, the existing projection matrices are updated with an eigenspace merging algorithm from Hall [50].