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To study this scenario, we considered the following cases with respect to the rate of update requests by MNNs.
Aboalsamh [4] proposed an incremental learning way and overcame the effect of concept drift phenomenon through accelerating the rate of update and updating the model continuously.
(4) After some lines of the signaling system come into operation, the rate of update is frequent, but the updating spare parts provided by the supplier fall short of actual requirement. .
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Therefore, the key problems are when to update the model and the rate of updating.
This is possible using an additional parameter which controls the rate of updates.
Since bandwidth updates require signaling, it is common to limit the rate of updates to reduce signaling costs.
The rate of updates decreases with higher values of α and with increase of offered throughput for our proposal.
where σ and ϱ l are used to control the rate of updating α and β l.
Further, in order to avoid errors caused by model drift, the updating process should only be implemented in a reliable manner, and the rate of updating can be controlled according to this reliability.
In order to avoid incorrect results caused by "model drift" introduced into the observation model, model updating should only be controlled in a reliable manner, and the rate of updating is based on reliability.
In order to avoid errors caused by model drift, the updating process should only be implemented in a reliable manner, and the rate of updating can be controlled according to this reliability.
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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