A no-regret generalization of hierarchical softmax to extreme multi-label classification

NeurIPS 2018 Marek WydmuchKalina JasinskaMikhail KuznetsovRóbert Busa-FeketeKrzysztof Dembczyński

Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels. Large label spaces can be efficiently handled by organizing labels as a tree, like in the hierarchical softmax (HSM) approach commonly used for multi-class problems... (read more)

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