# Evaluating Bregman Divergences for Probability Learning from Crowd

## TL;DR

This paper explores the use of Bregman divergences as objective functions for training probabilistic models from crowd-sourced data, emphasizing the importance of careful optimization in neural networks.

## Contribution

It introduces models that utilize Bregman divergences for probability distribution learning from crowdsourcing data, highlighting optimization considerations.

## Key findings

- Proper objective function selection is crucial for effective learning.
- Optimization strategies significantly impact model performance.
- Bregman divergences can effectively model crowd-derived probability distributions.

## 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.