During classification, a probabilistic matching measure is used to search through a finite graph to find the best assignment of geometric parts to the functional structures of each class.
During classification, false positives result in inconsistency outcomes.
During classification, a data point xi is assigned to a class according to the class label of the closest CV.
During classification, some classifiers from the outlier classes may give small classification probabilities when the classifiers estimate that the pattern is not an outlier.
During classification, the distances from both hyperplanes determine the appropriate bin, but the class is determined as the majority class in the bin.
During classification, the decision of every tree is counted as one vote and the overall outcome is the class with the majority of votes.
During classification, minhash values within each minhash signature resulting from map reduction aggregation are looked up within the Strand training data structure to determine an accurate estimation of Jaccard similarity between the query sequence and all known classes.
During classification, x will be reconstructed in the input space by every P(φ(x)) i, then m reconstruction results x 1 ′, …, x m ′ can be obtained, the distance between x and each x i ′ (also called reconstruction error) is calculated, and x will be assigned to the class whose KPCA model produces the minimum reconstruction error.
During classification, the class label of an unknown sample s is determined as: (2) C (s ) = arg min C j d i s (μ C j, s ) where dis x, y ) is a distance function or: (3) C (s ) = arg max C j s i m (μ C j, s ) where sim x, y ) is a similarity metric.
During the classification step, an EEG signal is allocated a certain class based on the location of its feature vector.
