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arXiv · 1901.10653

Evaluating Bregman Divergences for Probability Learning from Crowd

F. A. Mena·R. Ñanculef
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Abstract

The crowdsourcing scenarios are a good example of having a probability distribution over some categories showing what the people in a global perspective thinks. Learn a predictive model of this probability distribution can be of much more valuable that learn only a discriminative model that gives the most likely category of the data. Here we present differents models that adapts having probability distribution as target to train a machine learning model. We focus on the Bregman divergences framework to used as objective function to minimize. The results show that special care must be taken when build a objective function and consider a equal optimization on neural network in Keras framework.

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F. A. Mena, R. Ñanculef. 2019-01-30. Evaluating Bregman Divergences for Probability Learning from Crowd. https://arxiv.org/abs/1901.10653

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