金沙澳门“舜耕讲坛”之六十

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)

主讲人概况:

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。

报告内容概况:

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.

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