  Logitboost is an influential boosting algorithm for classification. In this
paper, we develop robust logitboost to provide an explicit formulation of
tree-split criterion for building weak learners (regression trees) for
logitboost. This formulation leads to a numerically stable implementation of
logitboost. We then propose abc-logitboost for multi-class classification, by
combining robust logitboost with the prior work of abc-boost. Previously,
abc-boost was implemented as abc-mart using the mart algorithm. Our extensive
experiments on multi-class classification compare four algorithms: mart,
abcmart, (robust) logitboost, and abc-logitboost, and demonstrate the
superiority of abc-logitboost. Comparisons with other learning methods
including SVM and deep learning are also available through prior publications.
