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We propose algorithms for ameliorating difficulties in fast approximate k Nearest Neighbors (kNN) classifiers that arise from imbalances among classes in numbers of samples, and from concentrations of samples in small regions of feature space. These problems can occur with a wide range of binning kNN algorithms such as k-D trees and our variant, hashed k-D trees. The principal method we discuss automatically...
The three common faults of rotating machinery, that is, imbalance, misalignment and rubbing, were simulated on Bently, vibration displacements at every sampling time have been measured according to a certain time interval by using eddy current displacement sensor, then the time-vibration displacements of the three faults have been got, and the corresponding figures of time-displacement were drawn...
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