Examples of 'euclidean distance' in a sentence
Meaning of "euclidean distance"
A mathematical measurement or metric used to calculate the straight-line distance between two points in a Euclidean space, often employed in various fields such as geometry, physics, and data analysis
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- The distance between two points defined as the square root of the sum of the squares of the differences between the corresponding coordinates of the points; for example, in two-dimensional Euclidean geometry, the Euclidean distance between two points a = (aₓ, a_y) and b = (bₓ, b_y) is defined as: d( textbf a, textbf b)=√
- The distance between two points defined as the square root of the sum of the squares of the differences between the corresponding coordinates of the points; for example, in two-dimensional Euclidean geometry, the Euclidean distance between two points a = (aₓ, a_y) and b = (bₓ, b_y) is defined as
- d( textbf a, textbf b)=√
How to use "euclidean distance" in a sentence
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euclidean distance
Euclidean distance is invariant under orthogonal transformations.
In this experiment the euclidean distance was used.
Euclidean distance and complete linkage method.
The target is to maximize the euclidean distance between the transmitted signals.
Euclidean distance is used to measure differences between segments.
Multivariate analysis was performed using hierarchical clustering based on average euclidean distance.
Euclidean distance was used as a measure of distance.
Popular choice is the Euclidean distance given by.
Euclidean distance between x and y.
Pearson correlation is very similar to Euclidean distance.
Euclidean distance is a statistical measure of distance between two points.
Generalizations are possible to metrics other than Euclidean distance.
A the euclidean distance between.
Average linkage clustering was performed using Euclidean distance.
Euclidean distance is calculated as.
See also
The distance may be a Euclidean distance.
Euclidean distance is widely used to compare any points of any dimension.
The basic concept of length originates from Euclidean distance.
Euclidean distance is used as a metric and variance is used as a measure of cluster scatter.
The most common distance metric used is Euclidean distance.
The euclidean distance between the two nearest opponents was calculated as a function of time.
The distance norm is usually taken to be the Euclidean distance.
Euclidean distance is equal to the number of neighbors that differ between two vertices.
Unsupervised hierarchical clustering was based on the Euclidean distance metric.
Euclidean distance was negatively correlated with transgressive segregation and generalized genetic variances.
In this embodiment the vector distance includes a Euclidean distance.
The Euclidean distance was deployed as a distance measure.
Pixels are then assigned to each cluster based on Euclidean distance.
We used the Euclidean distance as a distance metric.
A commonly used distance metric for continuous variables is Euclidean distance.
Compute the Euclidean distance for one dimension.
The aim is to minimize the error relative to the Euclidean distance.
The Euclidean distance is a quantitative metric between two points.
The loss function can be described using a Euclidean distance function.
Euclidean Distance is the most commonly used distance measure.
Distance returns the Euclidean distance between the two points.
We calculated the similarity coefficient using the average Euclidean distance.
The Euclidean distance between the two points is its magnitude.
Similarity between the cells is inversely proportional to the Euclidean distance.
It maintains Euclidean distance constraints between n points by means of a geometric algorithm.
The pairwise distance between sequences is calculated using Euclidean distance measure.
How to calculate the Euclidean distance from many elements to an origin.
The pair wise distance between sequences is calculated using Euclidean distance measure.
A combination of curvature information and Euclidean distance is used to improve the registration performance.
We used GenePattern for unsupervised hierarchical clustering of the samples based on Euclidean distance.
Consider the problem of finding the minimum Euclidean distance to an affine subspace.
Restricting the Euclidean distance function gives the irrationals the structure of a metric space.
This is due to a relationship between the Euclidean distance and the hamming distance.
Distances between pairs of signatures are computed using classic algorithms such as the Euclidean distance.
Hierarchical clustering of selected genes was performed using Euclidean distance measurements as similarity comparison.
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Examples of using Euclidean
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Euclidean spaces also generalize to higher dimensions
In this experiment the euclidean distance was used
Euclidean distance is invariant under orthogonal transformations
Examples of using Distance
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Toll or long distance charges may apply
Distance travelled to work and time taken
In the symbol the distance to be measured is dark
