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The curve is examined for areas of large distances to the closest data points.
We propose an algorithm enabling clients to locate tentatively the closest data replica without prior request to any metadata node.
The margin is the distance between the separation plane and the closest data point.
This optimal separating hyperplane (OSH) w: wx + b = 0 maximizes the margin of the closest data points.
The separation plane divides the data into Class 1 and Class 2. The margin is the distance between the separation plane and the closest data point.
Secondly, the error bound is minimized by maximizing the margin, that is, the minimal distance between the hyperplane and the closest data points.
Similar(44)
The lower the standard deviation, the closer data points tend to be to the mean, and higher means people's political views are spread further out along the scale.
The better the agreement between ΔEELV measurements, the closer data pairs will lie along the horizontal radial axis.
The closer data point should get more resources because it is more likely to have the same affective level as user's preference.
Suppose we have a time series data to be modeled using a clustering method to produce a dendrogram T i i.e., a cluster model in which the close data points are clustered together in a hierarchical tree form.
The higher the agreement, the closer data pairs will lie along the radial axis.
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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