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Is there any guidance available for training on very noisy data, when Bayes error rate (lowest possible error rate for any classifier) is high? For example, I wonder if deliberately (not due to memory or numerical stability limitations) lowering the batch size or learning rate could produce a better classifier.

I found so far some general recommendations, not specific for noisy data: Tradeoff batch size vs. number of iterations to train a neural network

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    $\begingroup$ Shouldn't it be the contrary? Batch learning is used as it cancels out noise due to all the samples taken...Whereas in the extreme case of batch Learning where batch size = 1 also called online learning results in very shaky convergence with spikes in loss. $\endgroup$ – DuttaA Feb 22 at 9:12

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