cbb6a3b884f4f88b3a8e3d44c636cbd8-Reviews.html

The authors study whether and when a hierarchical classifier can be more beneficial than its flat counterpart. They proof a generalization bound that provides an explanation when a flat and when a hierarchical classifier should be used. Additionally, the authors provide an approach for logistic regression and naive Bayes classifiers, which enables pruning of nodes in large-scale hierarchies. Quality: The authors consider a very interesting and up-to-date problem. Therefore I was very glad to read this paper. The first bound obtained by the authors is very interesting and indeed provides an explanation of existing empirical results.