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Multi-label learning refers to methods for learning a set of functions that assigns a set of relevant labels to each instance. One of popular approaches to multi-label learning is label ranking, where a set of ranking functions are learned to order all the labels such that relevant labels are ranked higher than irrelevant ones. Rank-SVM is a representative method for label ranking where ranking loss...
Multi-label learning refers to methods for learning a classification function that predicts a set of relevant labels for an instance. Label embedding seeks a transformation which maps labels into a latent space where regression is performed to predict a set of relevant labels. The latent space is often a low-dimensional space, so computational and space complexities are reduced. However, the choice...
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