International Conference of Kernel-based Approximation Methods in Data Analysis was successfully held

Time: 2018-05-28 19:31:00

From May 25 to 27, 2018, International Conference of Kernel-based Approximation Methods in Data Analysis was successfully held in Guangzhou. Following the first conference in 2017, the conference was successfully held by the School of Mathematical Sciences of South China Normal University. The scientific committee is made up of some professors at home and abroad, led by Professor Yuesheng Xu from Sun Yat-sen University, including Tao Tang from the South University of Science and Technology, Professor Zongmin Wu from Fudan University, Professor Zhouping Xin from the Chinese University of Hong Kong, Professor Ian Sloan from University of New South Wales, Professor Robert Schaback from the University of Gottingen, Professor Michael Griebel from the University of Bonn and so on. Hundreds of people from  South China Normal University, Sun Yat-sen University, Jinan University, Shantou University,  Shenzhen University, as well as from the United States, Canada, Germany, Australia, Hong Kong and Taiwan attended these two conferences.

Professors from all over the world gathered in this conference made some wonderful reports, including Professor Gregory Fasshauer and Charles Micchelli from the United States, Professor Martin Buhmann and Armin Iske from Germany, Professor Dingxuan Zhou from Hong Kong, and Professor Lintian Luh from Taiwan and so on. This conference talked about Reproduce Kernel Functions, Radial Basis Functions, high-dimensional data approximation methods, numerical solutions of partial differential equations and sparse machine learning methods. The conference deeply discussed the data analysis and machine learning methods in the international frontier research subjects, and carried out a number of international cooperation research, effectively laid a solid theoretical foundation for the upgrading of the industry in our country, and promoted the cultivation of high level professional and technical talents in the intelligent science.