• Corpus ID: 88520815

Dealing with a large number of classes -- Likelihood, Discrimination or Ranking?

@article{Barber2016DealingWA,
  title={Dealing with a large number of classes -- Likelihood, Discrimination or Ranking?},
  author={David Barber and Aleksandar Botev},
  journal={arXiv: Machine Learning},
  year={2016}
}
We consider training probabilistic classifiers in the case of a large number of classes. The number of classes is assumed too large to perform exact normalisation over all classes. To account for this we consider a simple approach that directly approximates the likelihood. We show that this simple approach works well on toy problems and is competitive with recently introduced alternative non-likelihood based approximations. Furthermore, we relate this approach to a simple ranking objective… 

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