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Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of ...
In the feature extraction, it is difficult to increase the interclass distance and reduce the intraclass variance according to the limited label information, resulting in easy misclassification of the ...
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