Two template-based classifiers, one based on Euclidean distance and a second based on a cross-correlation were then applied to measure the separation of the input output relationship.
Cosine similarity based on Euclidean distance is currently one of the most widely used similarity measurements.
Claudin-Low and Normal centroids were generated, and samples were assigned to one or the other class based on euclidean distance to the class centroid.
These functions were based on Euclidean distance function.
Conservation rankings based on movement data were uncorrelated with those based on Euclidean distance.
Calculating the difference between the patterns based on Euclidean distance are called deterministic form.
One of the most popular similarity measures is cosine similarity, which is based on Euclidean distance.
Currently, a simple linear classifier based on Euclidean distance is used for classification.
Tracking is implemented based on Euclidean distance, as shown in Eq. 6.
Interestingly, the novelty measures based on Euclidean distance generally performed better than those using Tanimoto distance.
The new function based on Euclidean distance in the last stage can effectively measure the similarity of time series.
