In this talk, I will present a large margin method for hierarchical
classification. The main focus here is to utilize the dependency
structure among classes to improve the classification performance of
flat classification. In such a situation, flat classification is
infeasible in the presence of a large number of dependent classes, which
occurs often in gene function discovery. Various hierarchical losses
will be discussed, in addition to an application to
gene function prediction.