# Active Learning For Identifying Function Threshold Boundaries (2005)

Brent Bryan, Jeff Schneider, Robert C. Nichol, Christopher J. Miller, Christopher R. Genovese, 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 $1-\alpha$ 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.