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We present a scalable and effective classification model to train multi-classboosting for multi-class classification problems. Shen and Hao introduced adirect formulation of multi- class boosting in the sense that it directlymaximizes the multi- class margin [C. Shen and Z. Hao, "A direct formulationfor totally-corrective multi- class boosting", in Proc. IEEE Conf. Comp. Vis.Patt. Recogn., 2011]. The major problem of their approach is its highcomputational complexity for training, which hampers its application onreal-world problems. In this work, we propose a scalable and simple stage-wisemulti-class boosting method, which also directly maximizes the multi-classmargin. Our approach of- fers a few advantages: 1) it is simple andcomputationally efficient to train. The approach can speed up the training timeby more than two orders of magnitude without sacrificing the classificationaccuracy. 2) Like traditional AdaBoost, it is less sensitive to the choice ofparameters and empirically demonstrates excellent generalization performance.Experimental results on challenging multi-class machine learning and visiontasks demonstrate that the proposed approach substantially improves theconvergence rate and accuracy of the final visual detector at no additionalcomputational cost compared to existing multi-class boosting.

A scalable stage-wise approach to large-margin multi-class loss based boosting