17. Learning: Boosting
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston Can multiple weak classifiers be ...
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PATRICK WINSTON: We've now almost completed our journey. This will be it for talking about several kinds of learning-- the venerable kind, that's the nearest neighbors and identification tree types of learning. Still useful, still the right thing to do if there's no reason not to do the simple thing. Then we have the biologically-inspired approaches. Neural nets. All kinds of problems with local maxima and overfitting and oscillation, if you get the rate constant too big. Genetic algorithms. Like neural nets, both are very naive in their attempt to mimic nature. So maybe they work on a class of problems. They surely do each have a class of problems for which they're good. But as a general purpose first resort, I don't recommend it. But now the theoris...
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PATRICK WINSTON: We've now almost completed our journey. This will be it for talking about several kinds of learning-- the venerable kind, that's the nearest neighbors and identification tree types of learning. Still useful, still the right thing to do if there's no reason not to do the simple thing. Then we have the biologically-inspired approaches. Neural nets. All kinds of problems with local maxima and overfitting and oscillation, if you get the rate constant too big. Genetic algorithms. Like neural nets, both are very naive in their attempt to mimic nature. So maybe they work on a class of problems. They surely do each have a class of problems for which they're good. But as a general purpose first resort, I don't recommend it. But now the theoris...
