Reducing computational costs in deep learning on almost linearly separable training data
Previous research in deep learning indicates that iterations of the gradient descent, over separable data converge toward the L2 maximum margin solution. Even in the absence of explicit regularization, the decision boundary still changes even if the classification error on training is equal to zero....
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| Format: | Article |
| Language: | English |
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Samara National Research University
2020-04-01
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| Series: | Компьютерная оптика |
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| Online Access: | http://computeroptics.smr.ru/KO/PDF/KO44-2/440219.pdf |