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Exact(3)
The aggregated distance to known classes, dist i (u j,c k ), is computed by ClassDist u j,c k ) based on the individual distances between each pair of instances (2).
For any pair of instances (x i, x j ) in the labeled data set X l, there is a constraint assigned, either must-link (ML) or cannot-link (CL).
Note that a kernel matrix Kω can be computed off-line for every pair of instances in D, i.e. as ⌊Kω⌋ij = kω xi,xj).
Similar(57)
The algorithm starts by going through all pairs of instances of the concept ({hat{C}}) (that is, all nodes).
For calculating the similarity between pairs of instances, the Euclidean distance with normalization on each variable was employed.
The Euclidean metric was previously used in step (2) to compute the distance between all pairs of instances in W.
The measure is defined as the average cosine similarity between all pairs of instances of a node's neighbourhood, averaged over all nodes.
This has similarities to the pivot-based methods which bridge the feature spaces through individual pairs of instances from each domain that are linked together.
Conventional online learning algorithms cannot be applied directly to one-pass AUC optimization because AUC is measured by a sum of losses defined over pairs of instances from different classes.
For pairs of instances in a constraint, for each possible combination of cluster assignments, the function is calculated and the instances are assigned to the clusters that minimally increases the error term h ∗=a r g m i n h O new.
The Rand index compares two clustering hypotheses taking into account all possible pairs of instances.
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