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I'm having a hard time visualizing Ranking SVM and would love help "drawing it out". Rank SVM is a multi-label multi-classification learning method, and Support Vector Machine was originally intended for single-label learning.

What are the differences imposed in rank SVM that allows for the novel structure of multi-label datasets?

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See this paper: Elisseeff, André, and Jason Weston. "A kernel method for multi-labelled classification." Advances in neural information processing systems. 2002.

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