Multi-label Learning
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چکیده
Multi-label learning is an extension of the standard supervised learning setting. In contrast to standard supervised learning where one training example is associated with a single class label, in multi-label learning one training example is associated with multiple class labels simultaneously. The multi-label learner induces a function that is able to assign multiple proper labels (from a given label set) to unseen instances. Multi-label learning reduces to standard supervised learning by restricting the number of class labels per instance to one.
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تاریخ انتشار 2017