Active Learning For Identifying Function Threshold Boundaries

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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Authors

Brent Bryan, Robert C. Nichol, Christopher R. Genovese, Jeff Schneider, Christopher J. Miller, Larry Wasserman

Abstract

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.