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Active Learning Strategies for Time-series
Acquiring labelled data for training supervised learning models is a barrier to a wider adoption of machine learning strategies in several industries. The data annotation task is generally complex and time-consuming. In this project, we will use raw unlabeled data generated by fiber sensors to reduce the burden of labelling data with problems within the fiber industry.
Faculty Supervisor:
Amilcar Soares;Vinicius P. da Fonseca
Student:
Partner:
Instrumar
Discipline:
Computer science
Sector:
Manufacturing
University:
Memorial University of Newfoundland
Program:
Accelerate
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