Your English writing platform
Discover LudwigExact(59)
The basic idea of squared distance minimization for curve and surface fitting is first presented.
We also adapt the recently discovered local second order squared distance function approximant to the parameter correction setup.
We repeat the squared distance minimization and update the parameters of the quadratic curve and surface by iterations until convergency.
In the former case the proof requires a delicate analysis of minimizing geodesics of the group and of the differentiability properties of the squared distance function.
Mean excess Welch squared distance.
mean of the squared distance within.
Each squared distance D ( i, k ) 2 consists of at most ℓ-bits.
The supervised anchorbased algorithm [21] chooses the subset of anchors that minimizes the sum of squared distance estimation errors.
v2: Similar to v1, but using the squared distance instead.
Linear discriminant can be derived using a measure of generalized squared distance.
Similar(1)
We then calculated the sum-squared distance (squared Euclidean distance) between the voxel pattern and each of the 2 models.
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