赌博网站 学术报告[2025] 120号

(高水平大学建设系列报告1222号)

报告题目: eyman-Pearson Classifier with Successive Convex Approximation for Imbalanced Data

报告人：彭衡 教授（香港浸会大学）

报告时间：2025年11月21日下午15:00-16:00

报告地点：深圳大学粤海校区汇星楼一号教室

内容摘要：The Neyman-Pearson (NP) paradigm in binary classification is developed as a new statistical approach for addressing the unequal importance of type I and type II errors in asymmetric statistical learning. It aims to create classifiers that minimize type II error while keeping type I error within a user-defined limit. However, most of the current NP classifiers involve a two-step process, where errors from the model estimation can accumulate during the second inference step, possibly resulting in unstable outcomes. This article is the first to attempt constructing NP classifiers using the differenceof convex approximation and the successive convex approximation algorithm from an empirical optimization standpoint. The proposed one-step classifiers adhere to NP oracle inequalities, which are the NP paradigm's equivalent to oracle inequalities in traditional binary classification. In addition to their appealing theoretical attributes, we demonstrate their numerical benefits in controlling prioritized errors through both simulations and real data analyses.

报告人简历：彭衡，现为香港浸会大学数学系教授，2003年从香港中文大学取得统计学博士学位，2003年-2006年在普林斯顿大学做博士后。他主要从事非参数与半参数模型、模型选择、高维数据建模、混合模型等领域的研究。他是IMS的会员，2011-2014担任Statistica Sinica副主编，现为Computational Statistics and Data Analysis副主编；曾做过Annals，JASA，JRSSB，Biometrika，Statistica Sinica等的评审。在统计学国际顶级期刊Annals，JASA, Biometrika, Statistica Sinica，TEST和Computational Statistics and Data Analysis上发表论文数十篇。

欢迎师生参加！

邀请人：胡湘红

赌博网站

2025年11月17日