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arXiv · 1606.06959

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

David Barber·Aleksandar Botev
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Abstract

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. This leads us to suggest a specific setting for the optimal threshold in the ranking objective.

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David Barber, Aleksandar Botev. 2016-06-22. Dealing with a large number of classes -- Likelihood, Discrimination or Ranking?. https://arxiv.org/abs/1606.06959

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