概率逻辑
计算机科学
人工智能
估计员
分类器(UML)
机器学习
概率分类
朴素贝叶斯分类器
融合
贝叶斯定理
贝叶斯网络
数据挖掘
贝叶斯概率
统计
数学
语言学
哲学
支持向量机
作者
Guodong Guo,Yun Fu,Charles R. Dyer,Thomas S. Huang
标识
DOI:10.1109/cvprw.2008.4563041
摘要
Human age prediction is useful for many applications. The age information could be used as a kind of semantic knowledge for multimedia content analysis and understanding. In this paper we propose a Probabilistic Fusion Approach (PFA) that produces a high performance estimator for human age prediction. The PFA framework fuses a regressor and a classifier. We derive the predictor based on Bayes’ rule without the mutual independence assumption that is very common for traditional classifier combination methods. Using a sequential fusion strategy, the predictor reduces age estimation errors significantly. Experiments on the large UIUC-IFP-Y aging database and the FG-NET aging database show the merit of the proposed approach to human age prediction.
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