Inference and Prediction in a Complex Discovery Process

复杂发现过程中的推理和预测

• 批准号: 0604394
• 负责人: Xiaotong Shen
• 金额: --
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0604394&HistoricalAwards=false
• 关键词: Inference; Prediction; Complex; Discovery; Process

Abstract With advances of computing technology, scientific and engineering investigation becomes increasingly complex. Powerful statistical techniques therefore are needed to exploit data with complex structure. The proposed project will be centered at design and analysis of inferential and prediction methods for problems involving complex statistical modeling that arise in, for instance, machine learning and data mining. The problems to be investigated include inference about a modeling process to account for modeling uncertainty in a modeling process, as well as prediction and inference in the contexts of multi-class margin classification and semi-supervised learning. The specific aims of the project, motivated by characteristics of complex modeling processes in our targeted applications, are focused on (1) the development of a novel theory of inference, as well as inferential procedures and computational tools, for comparing complex modeling processes; (2) the development of multi-class margin classification techniques; (3) the development of novel techniques for semi-supervised learning; and (4) the specific development of techniques for the targeted applications including object tracking, cancer genomics classification, and text categorization. For the targeted applications, the PI will collaborate with other scientists and engineers. The proposed educational program will train graduate students for research in statistics. Success of this project will bring tremendous benefits to fundamental scientific and engineering research, particularlyin automatic machine processing to combine humans' intelligence with machine's speed, in mining data with complex structure, and in biomedical research.

摘要随着计算机技术的发展，科学和工程调查变得越来越复杂。因此，需要强大的统计技术来利用具有复杂结构的数据。拟议的项目将集中在推理和预测方法的设计和分析，涉及复杂的统计建模问题，例如，机器学习和数据挖掘。要调查的问题包括推理的建模过程中考虑建模过程中的建模不确定性，以及预测和推理的背景下，多类边缘分类和半监督学习。该项目的具体目标，在我们的目标应用程序的复杂建模过程的特点的动机，集中在（1）一个新的推理理论的发展，以及推理程序和计算工具，比较复杂的建模过程;（2）多类边缘分类技术的发展;（3）新技术的发展半监督学习;以及（4）针对目标应用的技术的具体开发，包括对象跟踪、癌症基因组学分类和文本分类。对于目标应用，PI将与其他科学家和工程师合作。 拟议中的教育计划将培养研究生从事统计学研究。该项目的成功将为基础科学和工程研究带来巨大的利益，特别是在自动机器处理方面，联合收割机将人类的智能与机器的速度相结合，挖掘具有复杂结构的数据，以及生物医学研究。