Your English writing platform
Discover LudwigSuggestions(5)
Exact(25)
From the variability viewpoint, LoessOnly methods performed better than PMOnly, although combined PMLoess methods exhibited best performance overall.
Initially, we tested five algorithms – nearest centroid, diagonal linear discriminant analysis (DLDA), compound covariate predictor, one-nearest and three-nearest neighbor predictor [ 28] and found that in our datasets nearest centroid and DLDA methods performed better than the others (data not shown) with similar performance to each other.
It was found that integrated methods performed better than just BDS.
Our proposed methods performed better than both the Kernel Density Estimation method and the TV MPLE method.
For feature selection case, all frequent itemsets combined methods performed better when compared to FI only counterpart.
All the DG methods performed better than the SE method for almost all the test cases, especially for those with strong discontinuities.
Similar(35)
The variable step methods perform better.
Table 2 shows that all local transfer learning methods perform better than non-transfer method MARS.
The patch-based denoising methods perform better than pixel-wise methods.
Sometimes other methods perform better for statistical reasons (theoretically shown in Section 11.4 of [41]).
However, denoising methods perform better than basic filtering such as median filter.
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