Efficient max-margin multi-label classification with applications to zero-shot learning

  title={Efficient max-margin multi-label classification with applications to zero-shot learning},
  author={Bharath Hariharan and S. V. N. Vishwanathan and Manik Varma},
  journal={Machine Learning},
The goal in multi-label classification is to tag a data point with the subset of relevant labels from a pre-specified set. Given a set of L labels, a data point can be tagged with any of the 2 L possible subsets. The main challenge therefore lies in optimising over this exponentially large label space subject to label correlations. Our objective, in this paper, is to design efficient algorithms for multi-label classification when the labels are densely correlated. In particular, we are… CONTINUE READING
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The challenge problem

  • C. Snoek, M. Worring, J. van Gemert, Geusebroek, J.-M, A. Smeulders
  • 2006
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