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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=652693
• 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突破*机器学习预测系统的主要瓶颈创造了新的机遇；即，他们对人类注释数据的依赖。这项研究正在开发新的信息适应方法，利用免费的辅助数据源来改进目标分类领域的预测系统，这将显着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性。 *一个关键成果将是一种新形式的 IT 服务，它将个人和*组织从昂贵的手动数据注释工作中解放出来。