Active Learning For Identifying Function Threshold Boundaries

We present an efficient algorithm to actively select queries for learning the boundaries separating a function domain into regions where the func- tion is above and below a given threshold. We develop experiment selec- tion methods based on entropy, misclassification rate, variance, and their combinations, and show how they perform on a number of data sets. We then show how these algorithms are used to determine simultaneously valid 1 − α confidence intervals for seven cosmological parameters. Ex- perimentation shows that the algorithm reduces the computation neces- sary for the parameter estimation problem by an order of magnitude.

Authors

• Brent Bryan
• Robert Nichol
• Christopher Genovese
• Jeff Schneider
• Christopher Miller University of Rochester
• Larry Wasserman

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