Semi-supervised multi-class Adaboost by exploiting unlabeled data

► We propose a semi-supervised learning method by using the multi-class boosting. ► It handles K-class classification without reducing into multiple two-class problems. ► The classification accuracy of base classifier requires only 1/K or better. ► Higher classification accuracy is achieved by exploiting the unlabeled data.