Machine Learning

机器学习

• 批准号: 1000231139-2015
• 负责人: Guo, Yuhong
• 金额: $ 8.74万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=693210
• 关键词: Machine; Learning

Emerging data resources in the big data era have created new opportunities to break the main bottleneck of machine learning prediction systems; namely, their reliance on human annotated data. This research is developing new information adaptation methods that exploit freely available auxiliary data sources to improve prediction systems for target classification domains, which will significantly reduce dependence on human supervision while increasing the autonomy of complex data analysis systems. A key outcome will be a new form of IT service that relieves individuals and organizations from the costly work of manual data annotation.

大数据时代的新兴数据资源为破坏机器学习预测系统的主要瓶颈创造了新的机会。也就是说，他们依赖人类注释的数据。这项研究正在开发新的信息适应方法，该方法利用可自由可用的辅助数据源来改善目标分类域的预测系统，这将大大减少对人类监督的依赖，同时增加复杂数据分析系统的自主权。 一个关键的结果将是一种新的IT服务形式，可以使个人和组织摆脱昂贵的手动数据注释的工作。