nips nips2005 nips2005-18 nips2005-18-reference knowledge-graph by maker-knowledge-mining
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Author: 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 function is above and below a given threshold. We develop experiment selection 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. Experimentation shows that the algorithm reduces the computation necessary for the parameter estimation problem by an order of magnitude.
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