CAREER: Machine Learning and Event Detection for the Public Good

职业：公益机器学习和事件检测

• 批准号: 0953330
• 负责人: Daniel Neill
• 金额: $ 53万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953330&HistoricalAwards=false
• 关键词: CAREER; Machine; Learning; Event; Detection

The goal of this research is to create and explore novel methods for detection of emerging events in massive, complex real-world datasets. The approach consists of new algorithms to efficiently and exactly find the most anomalous subsets of a large, high-dimensional dataset, as well as methodological advances to incorporate incremental model learning from user feedback into event detection, incorporate society-scale data from emerging, transformative technologies such as cellular phones and user-generated web content, and augment event detection by creating methods and tools for event characterization, explanation, visualization, investigation and response. The experimental research is integrated with a multi-pronged educational initiative to incorporate machine learning into the public policy curriculum through development of courses and seminars, workshops in machine learning and policy research and education, and establishment of a new Joint Ph.D. Program in Machine Learning and Policy. The results of this project will be incorporated into deployed event surveillance systems and applied to the public health, law enforcement, and health care domains, enabling more timely and accurate detection of emerging outbreaks of disease, prediction of emerging hot-spots of violent crime, and identification of anomalous patterns of patient care. Project results, including publications, software, and datasets, will be disseminated via project web site (http://www.cs.cmu.edu/~neill/CAREER).

这项研究的目标是创建和探索用于检测大规模、复杂的现实世界数据集中出现的事件的新方法。 该方法包括新算法，可有效、准确地找到大型高维数据集的最异常子集，以及方法论进步，可将用户反馈的增量模型学习纳入事件检测，将新兴的、变革性的社会规模数据纳入其中。移动电话和用户生成的网络内容等技术，并通过创建事件表征、解释、可视化、调查和响应的方法和工具来增强事件检测。 实验研究与多管齐下的教育计划相结合，通过开发课程和研讨会、机器学习和政策研究与教育研讨会以及建立新的联合博士项目，将机器学习纳入公共政策课程。机器学习和政策计划。 该项目的结果将被纳入已部署的事件监测系统中，并应用于公共卫生、执法和医疗保健领域，从而能够更及时、准确地发现新出现的疾病爆发，预测新出现的暴力犯罪热点，以及识别患者护理的异常模式。 项目成果，包括出版物、软件和数据集，将通过项目网站 (http://www.cs.cmu.edu/~neill/CAREER) 传播。