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Active Learning

Active Learning. Lots of data, very few labels Choose unknowns to be labeled Varying methods of choosing this unknown Hopefully, will find the best classifier with very small number of examples. Maximum Curiosity.

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Active Learning

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  1. Active Learning

  2. Lots of data, very few labels • Choose unknowns to be labeled • Varying methods of choosing this unknown • Hopefully, will find the best classifier with very small number of examples

  3. Maximum Curiosity • Generate new training sets by taking known data and adding assumed values for all unknowns • Run those through a learner and do statistics on results • Assume highest r value (cross-validated correlation coefficient) results from correct pairing

  4. Terrible Graphics Additive Curiosity Variant: Sum, not max

  5. Minimum Marginal Hyperplane • Based on Support Vector Machines • After learning SVM on known data, pick unknowns closest to boundary and repeat • Takes advantage of geometric features of SVMs

  6. Maximum Entropy • Calculate entropy of assumed datasets • Assume that the most informative item is that which is most uncertain (highest entropy)

  7. Entropic Tradeoff • Choose a mix of easily-classified and highly informative • At each step, choose both highest and lowest entropy unknowns to classify

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