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Given a sorted pattern list; the equivalence class expansion process considers ordered pairs of patterns in the class for expansion.
In general, 4(3) effective semi-diurnal tidal pairs of patterns are obtained, which represent ∼2/3 total variances of the origin data set.
A new pair-wise measure, which is computed between pairs of patterns rather than pairs of classifiers, is also proposed for two-class problems.
Assume that there are totally TC unused patterns; TC pairs of patterns are selected to perform the data embedding operation, where the distance of pattern pair owns the minimal distance.
Unlike the majority of the metric learning algorithms proposed so far, it can model non-linear relationships among data still providing a natural out-of-sample extension to novel pairs of patterns.
In this paper we present Similarity Neural Networks (SNNs), a neural network model able to learn a similarity measure for pairs of patterns, exploiting a binary supervision on their similarity/dissimilarity relationships.
Similar(48)
A mean of absolute difference of bicoherence (BMAD) is calculated for each pair of patterns.
A checked cell indicates a possible match in the adoption of the corresponding pair of patterns.
That is, first compute the set S of all frequent homomorphic patterns, and then do the maximality check and eliminate non-maximal patterns by checking the specificity relation on every pair of patterns in S.
For instance, in the case of two proteins belonging to the same family and showing very similar global folds, a GScore of 70%% for a given pair of patterns could implicate that such patterns might be exploited for the search of ligands able to discriminate between both proteins.
The pair of patterns "D2 or D3" would not have been identified as the designated pattern by considering D2 and D3 alone because D3 ranked low for reasons discussed above.
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