We present improved methods of using structured SVMs in a large-scale
hierarchical classification problem, that is when labels are leaves, or sets of
leaves, in a tree or a DAG. We examine the need to normalize both the
regularization and the margin and show how doing so significantly improves
performance, including allowing achieving state-of-the-art results where
unnormalized structured SVMs do not perform better than flat models. We also
describe a further extension of hierarchical SVMs that highlight the connection
between hierarchical SVMs and matrix factorization models.