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| • |
If we want to
encourage cooperation,
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we compare the
average of all the
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predictors with
the target and train to
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reduce the
discrepancy.
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– |
This
can overfit badly. It makes the
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model
much more powerful than
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training
each predictor separately.
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| • |
If we want to
encourage specialization
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we compare each
predictor separately
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with the target
and train to reduce the
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average of all
these discrepancies.
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– |
Its
best to use a weighted average,
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where
the weights, p, are the
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probabilities
of picking that “expert”
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for
the particular training case.
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