Published on Sep 14, 2012http://data.linkedin.comCrowdsourcing is a great tool to collect data and support machine learning — it is the ultimate form of outsourcing. But crowdsourcing introduces budget and quality challenges that must be addressed to realize its benefits.In this talk, I will discuss the use of crowdsourcing for building robust machine learning models quickly and under budget constraints. I’ll operate under the realistic assumption that we are processing imperfect labels that reflect random and systematic error on the part of human workers. I will also describe our “beat the machine“ system engages humans to improve a machine learning system by discovering cases where the machine fails and fails while confident on being correct. I’ll use classification problems that arise in online advertising.Finally, I’ll discuss our latest results showing that mice and Mechanical Turk workers are not that different after all.About the Speaker:Panos Ipeirotis is an Associate Professor and George A. Kellner Faculty Fellow at the Department of Information, Operations, and Management Sciences at Leonard N. Stern School of Business of New York University.His recent research interests focus on crowdsourcing and on mining user-generated content on the Internet. He received his Ph.D. degree in Computer Science from Columbia University in 2004, with distinction.He has received three “Best Paper“ awards IEEE ICDE 2005, ACM SIGMOD 2006, WWW 2011, two “Best Paper Runner Up“ awards JCDL 2002, ACM KDD 2008, and is also a recipient of a CAREER award from the National Science Foundation.
via ▶ Crowdsourcing: Achieving Data Quality with Imperfect Humans – YouTube.
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