Understand
We study the problem of reducing the amount of labeled training data required to train supervised classification models.
- We approach it by leveraging Active Learning, through sequential selection of examples which benefit the model most.
- Selecting examples one by one is not practical for the amount of training examples required by the modern Deep Learning models.
- We consider the mini-batch Active Learning setting, where several examples are selected at once.
Built on
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James MacQueen · 1967
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An analysis of approximations for maximizing submodular set functions—ii
Marshall L Fisher, George L Nemhauser, and Laurence A Wolsey · 1978
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Information-based objective functions for active data selection
David JC MacKay · 1992
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Pattern recognition and machine learning
Christopher M Bishop · 2006
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Batch mode active learning and its application to medical image classification
Steven CH Hoi, Rong Jin, Jianke Zhu, and Michael R Lyu · 2006
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The p-median problem: A survey of metaheuristic approaches
Nenad Mladenović, Jack Brimberg, Pierre Hansen, and José A Moreno-Pérez · 2007
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