Grant: Canada Research Chairs

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.