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I was reading in a On the Decision Boundary of Deep Neural Networks that the final layer of a MLP can be equated to an SVM and can generate decision boundaries similar to methods with SVM. I was wondering if using this boundary detection method or another can you quantify how much probability a model assigns to each bin where a bin is a class. So for example, if a project of an input before the final layer has a SVM margin of let's say 4 from the boundaries and classification 1 and 2, can we determine how much probability it'll give class 1 or 2 after the final layer?

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