  Actively sampled data can have very different characteristics than passively
sampled data. Therefore, it's promising to investigate using different
inference procedures during AL than are used during passive learning (PL). This
general idea is explored in detail for the focused case of AL with
cost-weighted SVMs for imbalanced data, a situation that arises for many HLT
tasks. The key idea behind the proposed InitPA method for addressing imbalance
is to base cost models during AL on an estimate of overall corpus imbalance
computed via a small unbiased sample rather than the imbalance in the labeled
training data, which is the leading method used during PL.
