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Pixel Club: Unsupervised Supervised Learning: Who Needs Labels Anyway?
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Guy Lebanon (Georgia Institute of Technology)
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Wednesday, 18.5.2011, 10:30
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Room 337-8 Taub Bld.
I will describe two recent results in unsupervised supervised learning (performing supervised learning tasks without labels). The first result concerns evaluating the accuracy of classifiers and regression models without labels. The second concerns training margin based classifiers such as SVM or logistic regression for high dimensional data without labels.
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