报告题目：Sparse Machine Learning in a Banach Space

报告人：许跃生

报告时间：2020年10月25日（星期六）8:30—9:30

报告地点：线上讲座（ZOOM ID: 935 1049 4677）

主办单位：数学与数量经济学院

主讲人简介：

许跃生，现任美国奥多明尼昂大学教授。本科、硕士毕业于中山大学，1989年在美国奥多明尼昂大学获得博士学位。曾任美国西弗吉尼亚大学Eberly讲席教授，雪城大学终身职正教授，中山大学国华讲席教授。现担任多个学术期刊编委。在剑桥大学出版社出版合著一部。论文发表在Applied and Harmonic Computational Analysis, SIAM Journal on Numerical Analysis, Inverse Problems, SIAM Journal on Imaging Science, IEEE Transactions on Medical Imaging等国际著名期刊，共计一百八十余篇。在计算数学、应用数学和基础数学的多个领域都做出过重要学术贡献。

报告内容简介：

We will report in this talk recent development of kernel based machine learning. We will first review a basic classical problem in machine learning - classification, from which we introduce kernel based machine learning methods. We will consider two fundamental problems in kernel based machine learning: representer theorems and kernel universality. We will then elaborate recent exciting advances in sparse learning. In particular, we will discuss the notion of reproducing kernel Banach spaces and learning in a Banach space.