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If you have a question about this talk, please contact Luning Sun.
How can we combine crowds of people into one giant mind in order to solve a problem at hand? Given many individual opinions regarding a question, how can we identify both the correct answer and the strongest members of the crowd? Crowdsourced solutions to various problems have proven to be of extremely high quality in many business situations. However, in many such situations, finding the best solution is equivalent to grading the tests of several students, without knowing the correct answers to the questions. I will show how machine learning techniques based on item-response theory can overcome this problem, and discuss some applications of this model.
This talk is part of the Cambridge Psychometrics Centre Seminars series.
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Other listsCambridge University Global Health Society Cambridge Cell Biology Seminar Series British Antarctic Survey's Natural Complexity: Data and Theory in Dialogue
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