Error bounds based on worst likely assignments use permutation tests to
validate classifiers. Worst likely assignments can produce effective bounds
even for data sets with 100 or fewer training examples. This paper introduces a
statistic for use in the permutation tests of worst likely assignments that
improves error bounds, especially for accurate classifiers, which are typically
the classifiers of interest.Comment: IJCNN 201