  Wepresentanovelcolumngenerationbasedboostingmethod for multi-class
classification. Our multi-class boosting is formulated in a single optimization
problem as in Shen and Hao (2011). Different from most existing multi-class
boosting methods, which use the same set of weak learners for all the classes,
we train class specified weak learners (i.e., each class has a different set of
weak learners). We show that using separate weak learner sets for each class
leads to fast convergence, without introducing additional computational
overhead in the training procedure. To further make the training more efficient
and scalable, we also propose a fast co- ordinate descent method for solving
the optimization problem at each boosting iteration. The proposed coordinate
descent method is conceptually simple and easy to implement in that it is a
closed-form solution for each coordinate update. Experimental results on a
variety of datasets show that, compared to a range of existing multi-class
boosting meth- ods, the proposed method has much faster convergence rate and
better generalization performance in most cases. We also empirically show that
the proposed fast coordinate descent algorithm needs less training time than
the MultiBoost algorithm in Shen and Hao (2011).
