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Old 04-13-2012, 11:42 PM
student322 student322 is offline
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Join Date: Apr 2012
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Default Coping with errors in training set

In Lecture 3, it was mentioned that when a human classifies hand-written digits, the error rate is around 2%. It seems then that training sets will also have errors (perhaps even introduced deliberately by mischevious individuals), and that the learning algorithm will thus be able to do no better than the error rate in the training set. Is this true? Are there any methods to efficiently detect and/or correct errors in the training set, aside from reviewing the whole set numerous times manually?
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