'pairwise relationship' is correct and usable in written English.
You can use it to describe a relationship between any two separate entities, such as two countries, two people, two concepts, etc. For example, "The pairwise relationship between the two countries has been strained, due to their ongoing trade disagreements."
Based on that pairwise relationship, we plug-in effective classification or clustering methods to detect attempts to gain unauthorized access.
Based on a novel motion feature to measure the relative motion of two patches, the SVM classifier learns their pairwise relationship.
To distinguish between two trajectories, we introduce a novel dissimilarity measure combined with a manifold learned tuning technique to capture the pairwise relationship between the two trajectories.
The resulting pairwise relationship graph consists of directed edges and AND edges.
The next step described below is transforming the pairwise relationship graph into a signaling arrow diagram.
First we will consider pairwise relationship graphs without any OR edges, i.e. OR-excluded graphs.
Existing methods only accommodate pairwise relationships such as the case where the suspect and the victim are siblings, for example.
We outline a K-way spectral clustering algorithm able to integrate pairwise relationships between the data samples.
(b) consists of averages of dot products which represent the averages of the pairwise relationships between different viewpoints.
(c) consists of averages of dot products which represent the averages of the pairwise relationships between different lighting conditions.
Pairwise relationships, also referred to as pairwise constraints, generally contain less information than class labels, but, in some contexts, are easier to obtain from human supervisors.
