Quote:
Originally Posted by ilya239
If the input distribution has high density near the target boundary, the sample will likely contain points near the boundary, so that large-margin or small-margin classifiers will be similar.
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You seem to have an interesting way of looking at the situation here, but I want to clarify the setup first. The sample here is the data set that is going to be used to train SVM, right? If so, can you explain why large and small margins will be similar in the above situation?