Exploiting Universum data in AdaBoost using gradient descent

• Published in: Image and vision computing Vol. 32; no. 8; pp. 550 - 557
• Main Authors: Xu, Jingsong, Wu, Qiang, Zhang, Jian, Tang, Zhenmin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2014
• Subjects: [formula omitted]-SVM; [formula omitted]AdaBoost; AdaBoost; Algorithms; Classification; Cost function; Functionally gradient materials; Gradient boost; Machine learning; Training; Universum; Universum; U-SVM; AdaBoost; UAdaBoost; Gradient boost

Recently, Universum data that does not belong to any class of the training data, has been applied for training better classifiers. In this paper, we address a novel boosting algorithm called UAdaBoost that can improve the classification performance of AdaBoost with Universum data. UAdaBoost chooses...