统计与管理学院2017年学术报告第80期

【主 题】 An overview of my research in Neyman-Pearson Classification

【报告人】 Tong Xin, 助教授

University of Southern California

【时 间】 2017年12月19日（星期二）15:30-16:10

【地 点】 上海财经大学统计与管理学院大楼1208会议室

【摘 要】In contrast to the classical binary classification paradigm that minimizes the overall classification error, the Neyman-Pearson (NP) paradigm seeks classifiers with a minimal type II error and a constrained type I error under a user-specified level, addressing asymmetric type I/II error priorities. NP paradigm is appropriate in applications such as cancer diagnosis, where a type I error (i.e., misdiagnosing a cancer patient as healthy) has more severe consequences than a type II error (i.e., misdiagnosing a healthy patient with cancer). This talk reviews the speaker's exisitng and current work on NP classification.

【嘉宾简介】

Xin Tong's research interests are in the areas of high dimensional statistical inference, learning theory, and social and economic networks. He has published papers in journals that include Journal of the Royal Statistical Society: Series B and the Journal of Machine Learning Research. Professor Tong won The Zellner Thesis Award in Business and Economic Statistics in 2013.