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Sorry, I just started in Deep Learning, so I am trying my best not to assume anything unless I am absolutely sure.

Going through comments here someone recommended this excellent paper on backpropagation Efficient BackProp by Yann LeCun. While reading I stuck at '4.5 Choosing Target Values'. I can't copy paste the text as pdf is not allowing it so posting the screenshot here. Most of the paper was clear to me but I couldn't understand exactly what the author was trying to convey for this specific part (see the image attached). If I understand correctly, is the author recommending to normalize target values between -0.90 and 0.90 for regression problem instead of -1,1 or is he saying that regression is wrong for classification because it will predict the wrong class?

enter image description here

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The paper is discussing binary classification target values only. Sometimes binary classification target values are encoded as either -1 or 1. These are the asymptotic limits of a logit-type function. Instead, it might be empirically more useful to encode binary classification target values to either -0.9 or .9.

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