Corpus ID: 12699975

Supplementary : Extreme Multi-label Learning with Label Features for Warm-start Tagging

@inproceedings{2017SupplementaryE,
  title={Supplementary : Extreme Multi-label Learning with Label Features for Warm-start Tagging},
  author={},
  year={2017}
}
  • Published 2017
  • Section 1 presents the pseudocodes for SwiftXML training and prediction algorithms. Section 2 reports complete set of experimental results comparing SwiftXML to various baselines in terms of both propensity-scored precisions (PSP1,PSP3,PSP5) as well as standard precisions (P1,P3,P5). Section 3 shows the derivations for individual steps of the alternating minimization algorithm used for node partitioning, as well as derivations of approximations for base classifier optimizations. 
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