Your English writing platform
Discover LudwigExact(4)
In Fig. 7 the improvement in performance of scoring over larger sized clusters on increasing data sizes is presented.
However, it is known that such an optimum placement problem associated with the variant data sizes is an NP-complete problem [29].
In total, including the effect of the unlabeled data usage, the improvement especially for small target data sizes, is quite substantial.
If the node's redundancy level is reduced by 1, though the receiving data sizes is unchanged, the sending data sizes is decreased, {vartheta}_u^t = left(frac{l+r}{l}cdot p+frac{l+2r}{l}cdot {p}^2+cdots +frac{l+zr}{l}cdot {p}^{z-1}+2right)lambda left|l= hr+x, x+zr le Rright.
Similar(56)
Different test data sizes were employed in the outer loop.
Indeed, as transparent compression and columnar storage have become increasingly common, actual data sizes are less and less meaningful.
Three different data sizes were compared in the three anonymization methods.
Associated characteristic values and data sizes are summarized in Table 1.
Because if the nodes' redundancy level in the adjacent loops are unchanged, the received data sizes are almost unchanged.
The execution times for varying data sizes are measured 10 times, and the average is presented in Table 3.
The data sizes are denoted as v i and v i ′, respectively, in which v i ′ > v i.
Write better and faster with AI suggestions while staying true to your unique style.
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