Your English writing platform
Discover LudwigSuggestions(3)
Exact(4)
As the exposure radiant was adjusted the all photoactivation methods, the polymerization depth obtained for all methods was quite similar.
Manevitz and Yousef [21] proposed another version of one-class SVM based on identifying outlier data as representative of the second class, and they applied their method to the standard Reuters[22] dataset and noted that their SVM methods was quite sensitive to the choice of representation and kernel.
In fact, the correlation between the outputs of the two normalization methods was quite limited, as evidenced by plotting the two corresponding genotype p-values for each probe (r2 = 0.07) - although we did observe a stronger correlation among the lower p-values (data not shown).
The landscape of methods was quite diverse with approaches based on stochastic learning, pattern mining, genetic programming, DP and so on.
Similar(56)
But while both described worlds stuttering between the real and unreal, their purposes and methods are quite distinct.
Nevertheless, the methods were quite time-consuming.
Iterative methods are quite robust against quantization and additive noise.
These methods are quite efficient in characterizing various texture motifs.
By comparison, the UK's methods are quite limited and occasionally labelled inadequate.
However, these two methods are quite different in regard to the method of quantification.
As it turns out, these methods are quite similar to those in the linear case.
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