Guest Editors‘ Introduction: On Applied Research in MachineLearning
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Input-agreement: a new mechanism for collecting data using human computation games
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Frontiers of a paradigm: exploring human computation with digital games
Proceedings of the ACM SIGKDD Workshop on Human Computation
Quality management on Amazon Mechanical Turk
Proceedings of the ACM SIGKDD Workshop on Human Computation
Combining human and machine intelligence in large-scale crowdsourcing
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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In recent years crowd-based and human computation systems have attracted increasing attention in science and industry. For applications that are driven by input from a multitude of human raters, ensuring data reliability and organizing an interactive workflow constitute a new challenge. In this paper we describe a novel approach to ensure data quality in crowd-based and human computation systems. The proposed algorithm features the potential for direct feedback and interactivity while producing little computational overhead.