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We can reduce
the variance term by averaging lots of
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models trained
on different datasets.
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This
seems silly. If we had lots of different datasets it
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would
be better to combine them into one big training
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set.
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With more training data there will be much
less variance.
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Weird
idea: We can create different
datasets by bootstrap
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sampling of our
single training dataset.
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This
is called bagging and it works surprisingly well.
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But if we have
enough computation its better to do the
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right Bayesian
thing:
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Combine
the predictions of many models using the
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posterior
probability of each parameter vector as the
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combination
weight.
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