[srean]

Its similar to L2regularized logistic regression not the pure logistic regression. Although in your defense pure logistic is not used that often. I find the large margin view very intuitive -- one finds a separator such that the labeled data points are separated and lie as far as possible from the line

[cultus]

Logistic regression is usually assumed to be regularized in machine learning. It's not often you find practical problems that don't need regularization.

[srean]

Indeed. You would notice that I had said as much. For the analogy between SVMs and LR to work you need L2 squared regression specifically, other regularizers, for example L1 wont work for the analogy. L2 squared is the key for the connection with Hilbert space and kernels to fall out automagically