Statistical Expertise in a Machine Learning Context

机器学习背景下的统计专业知识

• 批准号: 9321402
• 负责人: Cullen Schaffer
• 金额: $ 10万
• 依托单位: CUNY Hunter College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-09-01 至 1997-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9321402&HistoricalAwards=false
• 关键词: Statistical; Expertise; Machine; Learning; Context

9321402 Schaffer The project is to learn about how statisticians would tackle the problems that serve as a central testbed for machine learning research (e.g., the UCI repositioning). In particular, the proposed work will be concerned with the following four questions: (1) What model classes and fitting procedures do statisticians choose when faced with supervised learning problems drawn from the UCI repository? (2) On what basis do they make their choices? (3) How are results of data analysis interpreted to make predictions? (4) How accurate are these predictions?

9321402 Schaffer这个项目是为了了解统计学家将如何处理作为机器学习研究的中心试验台的问题(例如，UCI重新定位)。特别是，拟议的工作将涉及以下四个问题：(1)当面临从UCI知识库中提取的监督学习问题时，统计学家选择什么模型类和拟合程序？(2)他们基于什么做出选择？(3)如何解释数据分析的结果来做出预测？(4)这些预测的准确性如何？