大连理工大学数学科学学院
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Two topics in learning with kernels

2017年05月31日 15:54  点击:[]

数学学院学术报告

 

报告题目:Two topics in learning with kernels

报告时间:201765日(周一)上午10-11

报告地点:创新园大厦A1101

报告人:张海樟 教授 (中山大学)

校内报告联系人:徐敏 副教授  联系电话:84708351-8141

报告摘要:

We report two pieces of our recent work on learning with kernels: admissible kernels for RKHS embedding of probability distributions, and margin error bounds for the SVM on Banach spaces. These are joint with my PhD student Liangzhi Chen.

 

Similarity measurement of two probability distributions is important in many applications of statistics. Embedding such distributions into a reproducing kernel Hilbert space (RKHS) has many favorable properties. The choice of the reproducing kernel is crucial in the approach. So far, studies in the literature have been focusing on characteristic kernels which ensure the embedding to yield a metric. We attempt to impose a sophisticated admissible criterion on the reproducing kernel in measuring the similarity of a class of probability distributions.

 

Support vector machines, which maximize the margin from patterns to the separation hyperplane subject to correct classification, have received remarkable success in machine learning. Recently, there have been much interest in developing large margin classification in Banach spaces. We establish a margin error bound for the SVM on reproducing kernel Banach spaces, thus supplying statistical justification for pursuing large margin classification in Banach spaces.

 

报告人简介:张海樟,男,2003年本科毕业于北京师范大学数学系,2006年硕士毕业于中科院数学所,2009年博士毕业于美国雪城大学数学系,20096- 20105月期间为密歇根大学博士后。20106月起担任中山大学教授、博士生导师。现为中山大学数据科学与计算机学院数据科学系系主任。主要的研究兴趣为应用调和分析与学习理论,在JMLR, ACHA, J. Complexity等发表专业论文三十余篇,代表性的工作为再生核巴拿赫空间理论和时频分析的Bedrosian恒等式。其与密西根大学Jun Zhang教授合作的基于再生核巴拿赫空间的分类理论入选 Cambridge University Press出版的《数学心理学新手册》。主持国家自然科学基金三项,包括2012年首届优秀青年基金。

 

 

                                                      数学科学学院

2017531

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