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First train a
model on all of the data
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Lets
assume it get the great majority of the cases
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right.
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Then train
another model on all the cases the first model
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got wrong plus
an equal number that it got right.
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This
focusses the resources on modelling the hard
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cases.
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Train a third
model focusssing on cases that either or
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both previous
models got wrong.
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Then
use a simple committee of the three models
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This is quite
effective for learning to recognize
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handwritten
digits, but it is also very heuristic.
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Can
we give it a theoretical foundation?
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