The corresponding function is ϕ BF (p, k), where p is a non-leaf node and k is the number of children to be selected for p. To avoid sparsity, we choose one of R + 1 features (1, • • • , R or >R) for some pre-defined R. To be precise, we fire two features per non-leaf node: one is node-specific and the other is shared among non-leaf nodes.
we append at most (I + 1)(R + 1) features to the global weight vector, where I is the number of non-leaf nodes.