專題演講 主講人：周珮婷教授（政治大學統計系）
題 目：美國職棒大聯盟投手的類別探索型資料分析
主講人：周珮婷教授（政治大學統計系）
時 間：110年4月16日(星期五)上午10:40-11:30
(上午10:20-10:40茶會於交大統計所428室舉行)
地 點：交大綜合一館427室
摘要
From two coupled Multiclass Classification (MCC) and Response Manifold Analytics (RMA) perspectives, we develop Categorical Exploratory Data Analysis (CEDA) on PITCHf/x database for the information content of Major League Baseball's (MLB) pitching dynamics. MCC and RMA information contents are represented by one collection of multi-scales pattern categories from mixing geometries and one collection of global-to-local geometric localities from response-covariate manifolds, respectively. These collectives shed light on the pitching dynamics and maps out the uncertainty of popular machine learning approaches. In the first part of the talk, I will talk about an indirect-distance-measure-based label embedding tree that leads to discovering asymmetry of mixing geometries among labels' point-clouds on the MCC setting. In the second part of the talk, using the CEDA approach to evaluate the reliability or uncertainty of all identifiable patterns in an extreme-K categorical sample problem will be demonstrated.
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