16. Learning: Support Vector Machines
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, we explore support ...
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PATRICK WINSTON: So where are we? We started off with simple methods for learning stuff. Then, we talked a little about a purchase of learning that we're vaguely inspired by. The fact that our heads are stuffed with neurons, and that we seemed to have evolved from primates. Then, we talked about looking at the problem and address the issue of [? phrenology ?] and how it's possible to learn concepts. But now, we're coming full circle back to the beginning and thinking about how to divide up a space with decision boundaries. But whereas, you do it with a neural net or a nearest neighbors or a ID tree. Those are very simple ideas that work very often. Today, we're going to talk about a very sophisticated idea that still has a implementation. So this needs to be...
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PATRICK WINSTON: So where are we? We started off with simple methods for learning stuff. Then, we talked a little about a purchase of learning that we're vaguely inspired by. The fact that our heads are stuffed with neurons, and that we seemed to have evolved from primates. Then, we talked about looking at the problem and address the issue of [? phrenology ?] and how it's possible to learn concepts. But now, we're coming full circle back to the beginning and thinking about how to divide up a space with decision boundaries. But whereas, you do it with a neural net or a nearest neighbors or a ID tree. Those are very simple ideas that work very often. Today, we're going to talk about a very sophisticated idea that still has a implementation. So this needs to be...
