The other day I woke up at three in the morning. Couldn't sleep. No good reason.
So I went to the living room and tried to work through a statistical problem that had been bugging me: how are support vector machines different from logistic regression? I know -- I'm sure it's been on your mind too.
Anyway, after much math and Googling, I discovered this paper, which clarified the whole thing. SVM and logistic regression are basically the same, except that they optimize for slightly different parameters. SVMs typically do better on smaller training sets, but logistic regression has better asymptotic properties.
And now you know.
PS - This is all very handy if you are trying to train computers to read blogs for you.
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