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).

这项研究的目的是创建和探索在大规模，复杂的现实世界数据集中发现新兴事件的新方法。 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,可视化，调查和响应。 实验研究与一项多管齐下的教育计划集成在一起，该计划通过开发课程和研讨会，机器学习和政策研究和教育的讲习班以及建立新的联合博士学位，将机器学习纳入公共政策课程。机器学习和政策中的计划。 该项目的结果将纳入部署的事件监视系统中，并应用于公共卫生，执法和医疗保健领域，使能够更及时，更准确地发现出现的疾病爆发，预测新兴的暴力犯罪热点以及对患者护理的无态模式的识别。 项目结果（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。