关键词: 距离函数, 损失函数, 正则化, AdaBoost算法

Abstract: According to weight update model via distance and lost function regularization, proposed by J.Kivinen and M.K.Warmuth, using relative entropy, Itakura-Saito, first order exponential approximation distance function, combined with relative entropy lost function, this paper devises three sorts of weight update method of weak classifier of AdaBoost. Using the UCI real datasets, the three algorithms AdaBoostRE, AdaBoostIE, AdaBoostEE are compared with three leading assembly classifier：Real AdaBoost, Gentle AdaBoost and Modest AdaBoost. Experimental results show promising performance of the proposed method.

Key words: distance function, loss function, regularization, AdaBoost algorithm