Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …

Supervised learning techniques construct predictive models by learning from a large
number of training examples, where each training example has a label indicating its ground …

Crowdsourcing is a computing paradigm where humans are actively involved in a
computing task, especially for tasks that are intrinsically easier for humans than for …

Any important data management and analytics tasks cannot be completely addressed by
automated processes. These tasks, such as entity resolution, sentiment analysis, and image …

Quality improvement methods are essential to gathering high-quality crowdsourced data,
both for research and industry applications. A popular and broadly applicable method is task …

This survey provides a comprehensive overview of the landscape of crowdsourcing
research, targeted at the machine learning community. We begin with an overview of the …

In order to improve the efficiency and utility of mobile crowdsourcing systems, this paper
proposes an incentive mechanism with privacy protection in mobile crowdsourcing systems …

Crowdsourcing is widely accepted as a means for resolving tasks that machines are not
good at. Unfortunately, Crowdsourcing may yield relatively low-quality results if there is no …

A crowdsourcing system, such as the Amazon Mechanical Turk (AMT), provides a platform
for a large number of questions to be answered by Internet workers. Such systems have …

Off-the-shelf smartphones have boosted large scale participatory sensing applications as
they are equipped with various functional sensors, possess powerful computation and …