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Rely on the
learning algorithm getting stuck in a different
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local optimum on
each run.
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A
dubious hack unworthy of a true computer scientist
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(but
definitely worth a try).
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Use lots of
different kinds of models:
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Different
architectures
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Different
learning algorithms.
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Use different
training data for each model:
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Bagging: Resample (with replacement) from the
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training
set: a,b,c,d,e -> a c c d d
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Boosting: Fit models one at a time. Re-weight each
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training
case by how badly it is predicted by the
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models
already fitted.
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This makes
efficient use of computer time because it does
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not bother to
back-fit models that were fitted earlier.
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