MIDAS: Model-Independent Training Data Selection Under Cost Constraints

In general, as the amount of training data is increased, a deep learning model gains a higher training accuracy. To assign labels to training data for use in supervised learning, human resources are required, which incur temporal and economic costs. Therefore, if a sufficient amount of training data...

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Bibliographic Details

Published in:	IEEE Access
Main Authors:	Gyoungdon Joo, Chulyun Kim
Format:	Article
Language:	English
Published:	IEEE 2018-01-01
Subjects:	Training data selection cost constraints model-independent deep learning active learning machine learning
Online Access:	https://ieeexplore.ieee.org/document/8540354/