Inference and Prediction in a Complex Discovery Process

摘要随着计算机技术的进步，科学和工程调查变得越来越复杂。因此，开发具有复杂结构的数据需要强大的统计技术。拟议的项目将集中于设计和分析推理和预测方法，以解决机器学习和数据挖掘等涉及复杂统计建模的问题。要研究的问题包括关于建模过程的推理以解释建模过程中的建模不确定性，以及在多类边界分类和半监督学习的背景下的预测和推理。根据我们目标应用中复杂建模过程的特点，该项目的具体目标集中在(1)开发用于比较复杂建模过程的新的推理理论以及推理过程和计算工具；(2)开发多类边缘分类技术；(3)开发用于半监督学习的新技术；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。拟议的教育计划将为统计学研究培养研究生。该项目的成功将给基础科学和工程研究带来巨大的好处，特别是在将人的智能与机器的速度相结合的自动机器处理方面，在挖掘复杂结构的数据方面，在生物医学研究方面。

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.