学术报告
Consistency Analysis of the Minimum Error Entropy Algorithm
题目： Consistency Analysis of the Minimum Error Entropy Algorithm
报告人：Prof. Qiang Wu (Middle Tennessee State University)
摘要: Information theoretical learning (ITL) is an important research area in signal processing and machine learning. It uses concepts of entropies and divergences from information theory to substitute the conventional statistical descriptors of variances and covariances. The empirical minimum error entropy (MEE) algorithm is a typical approach falling into this framework and has been successfully used in both regression and classification problems. In this talk, I will discuss the consistency analysis of the MEE algorithm. For this purpose, we introduce two types of consistency: The error entropy consistency and the regression consistency. A surprising result is that the regression consistency holds when the bandwidth parameter is sufficiently large. Regression consistency of two classes of special models is shown to hold with fixed bandwidth parameter. These results illustrate the complication of the MEE algorithm.
时间：5月11日（周三）下午4:00-5:00
地点：首都师大北一区教学楼707教室
欢迎全体师生积极参加！
