报告名称：Convergence for Kernel Minimum Error Entropy Principle

报告专家：胡婷

专家所在单位：西安交通大学

报告时间：2023年11月22号(周三)下午3:00-5:30

报告地点：学院201报告厅

专家简介：胡婷，西安交通大学管理学院教授，主要从事机器学习领域中算法的数学理论研究，研究成果主要发表在Applied and Computational Harmonic Analysis，Journal of Machine Learning Research，IEEE Transactions on Signal Processing，Inverse Problems，Constructive Approximation等刊物上。

报告摘要：Information theoretic learning is a learning paradigm that uses concepts of entropies and divergences from information theory. A variety of signal processing and machine learning methods fall into this framework. Minimum error entropy principle is a typical one amongst them. In this talk, we study a kernel version of minimum error entropy methods that can be used to find nonlinear structures in the data. We show that the kernel minimum error entropy can be implemented by kernel based gradient descent algorithms with or without regularization. Convergence rates for both algorithms are deduced.