关键词: 标记分布学习, 最大熵模型, 拟牛顿法, 目标函数选择

Abstract: Label distribution learning is a new machine learning paradigm proposed in recent years. In theory, this paradigm can be seen as a generalization of multi-label learning paradigm. Previous studies show that label distribution learning paradigm is an effective learning paradigm. It can solve some label ambiguity problems effectively. For label distribution learning, a number of special algorithms which have good prediction effect have been proposed. For these special algorithms, this paper proposes a generalization frame of label distribution learning. In this learning frame, a special algorithm consists of three parts, they are target function, output model and optimization  algorithm. This paper studies the part of target function in this generalization frame. In order to study the relationship between prediction effect of a label distribution learning algorithm and different target functions, this paper selects 7 representative distances. Based on the characteristics of each distance and experiment results of 5 real label distribution learning datasets, this paper proposes some suggestions how to choose a target function.

Key words: label distribution learning, maximum entropy model, quasi-Newton method, selection of target functions