  This paper is mainly concerned with the question of how to decompose
multiclass classification problems into binary subproblems. We extend known
Jensen-Shannon bounds on the Bayes risk of binary problems to hierarchical
multiclass problems and use these bounds to develop a heuristic procedure for
constructing hierarchical multiclass decomposition for multinomials. We test
our method and compare it to the well known "all-pairs" decomposition. Our
tests are performed using a new authorship determination benchmark test of
machine learning authors. The new method consistently outperforms the all-pairs
decomposition when the number of classes is small and breaks even on larger
multiclass problems. Using both methods, the classification accuracy we
achieve, using an SVM over a feature set consisting of both high frequency
single tokens and high frequency token-pairs, appears to be exceptionally high
compared to known results in authorship determination.
