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Data collection and labeling is one of the main challenges in employing machine learning algorithms in a variety of real-world applications with limited data.
“Extensions of Lipschitz mappings into a Hilbert space”
William Johnson and Joram Lindenstrauss · 1984
Earlier work this paper cites.
“An exact algorithm for maximum entropy sampling”
Chun-Wa Ko, Jon Lee and Maurice Queyranne · 1995
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“A comparison of some error estimates for neural network models”
Robert Tibshirani · 1996
Earlier work this paper cites.
“Gradient-based learning applied to document recognition”
Yann LeCun, Léon Bottou, Yoshua Bengio and Patrick Haffner · 1998
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“Statistical pattern recognition: A review”
Anil Jain, Robert.. Duin and Jianchang Mao · 2000
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“Interactive machine learning”
Jerry Fails and Dan Olsen · 2003
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“Representative sampling for text classification using support vector machines”
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