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Kernel Methods
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Kernel Methods
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1:39:20
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With linear methods, we may need a whole lot of features to get a hypothesis space that's expressive enough to fit our data -- there can be orders of magnitude more features than training examples. While regularization can control overfitting, having a huge number of features can make things computationally very difficult, if handled naively ...
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