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In its present form, the algorithm is not rigorous in the sense that boundary conflicts are ignored.
In form, the algorithm minimizes a least-square loss functional adding a coefficient-based ℓ2-penalty term over a linear span of features generated by a kernel function.
In the following, all the ingredients are assembled together to form the algorithm that we call OSLOM (Order Statistics Local Optimization Method).
We developed a new algorithm that dynamically identifies salient SemRep output, and then evaluated its utility by comparing its performance to that of a conventional summarization schema, as well as two of the individual metrics which form the algorithm.
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In their current form, the algorithms developed in this paper could be directly applied to data from other railway systems.
Once a cluster is formed, the algorithm repeats to form new clusters until no further clustering is possible.
Furthermore, a new iteration form of the algorithm is designed, improving the developing efficiency and run speed.
First, the Lagrangian form of the algorithm is reviewed, and then the algorithm is extended to the Eulerian frame of reference.
This estimator has the same form as the algorithm defined by (63).
In the final form of the algorithm we adjusted 20 parameters (see Table 3, which also contains the final parameter values after optimization).
The present form of the algorithm does not consider priorities.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com