勷勤数学•专家报告

题      目：A CVaR-based Programming for SVM with Uncertain Information

报  告  人：王宜举  教授  (邀请人：陈艳男)

                                    曲阜师范大学

时      间：11月20日  14：30-15：30

地     点：数科院西楼二楼会议室

报告人简介:

       曲阜师范大学教授，博士生导师，管理学院院长，享受国务院特殊津贴专家。中国科学院博士,香港理工大学和南京师范大学博士后。主要从事最优化的理论与算法研究，发表有一定学术影响力的论文80多篇。主持（完成）国家自然科学基金5项、省部级科研项目7项。获教育部和山东省自然科学二、三等奖5项。多次到香港城市大学、香港理工大学和澳大利亚的科廷大学进行学术访问和交流。出版运筹学专业研究生统编教材《非线性最优化理论与方法》一部。先后被重庆大学、国防科技大学、北京交通大学、海南大学和重庆师范大学聘为主讲教师为该校运筹学专业的研究生和博士生讲授《最优化方法》。

摘      要：

       For the parallel support vector machine problem with uncertain information on the observation, by characterizing the violation of the positive class training data to the ``upper" support hyperplane and that of the negative class training data to the ``lower" support hyperplane via the conditional value at risk (CVaR), we establish a CVaR-based optimization model. For the model, we first show that it is a good convex approximation to the basic chance-constrained optimization model for the problem, then with the help of Lagrange duality theory, we transform it into a deterministic semi-definite programming(SDP) which can be numerically solved by the state-of-the-art SDP solvers. Numerical experiments conducted on the artificial and the real benchmark datasets show the validity and the efficiency of the proposed model.

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