基于信息理论的分类问题研究
Baogang Hu
Professor
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS)
Abstract: 不同于传统分类问题研究中应用分类误差为学习目标，本讲座介绍基于信息理论为优化目标的分类问题研究。我们主要针对互信息开展研究。
由此可以导出代价缺失学习(Cost-free learning)。该学习方法无需用户给出错分代价信息。它还可扩展到拒识分类学习(Abstaining learning)。在介绍贝叶斯分类器与互信息分类器的基本差异后，本讲座给出了若干数据仿真实例，并对互信息分类器的优缺点进行了总结。
Bio: Prof. Baogang Hu received his Ph.D. degree in 1993 from Department of
Mechanical Engineering, McMaster University, Canada. Currently, he is a
professor of National Laboratory of Pattern Recognition, Institute of
Automation, Chinese Academy of Sciences (CAS), Beijing, China. From 2000
t0 2005, he served as the Chinese Director of "Chinese-French Laboratory of
Information, Automation and Applied Mathematics"(LIAMA) sponsored by
CAS(China), INRIA/CNRS/CIRAD(France). His current researches include
machine learning and plant growth modelling.