标题： 从众包中评估Bregman散度用于概率学习 显示英文标题

标题： Evaluating Bregman Divergences for Probability Learning from Crowd

摘要： 众包场景是具有某些类别上的概率分布的一个很好的例子，它展示了全球视角下人们的想法。 学习该概率分布的预测模型比仅仅学习一个判别模型（给出数据最可能类别的判别模型）更有价值。 在这里我们提出了不同的模型，这些模型以概率分布为目标来训练机器学习模型。 我们专注于Bregman散度框架，并将其用作目标函数以最小化。 结果表明，在构建目标函数以及在Keras框架中的神经网络上进行相等优化时，必须特别注意。 显示英文摘要

摘要： 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.