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Add an extra
component with value 1 to each feature
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vector. The
bias weight on this component is minus the
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threshold. Now we
can forget the threshold.
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Pick training
cases using any policy that ensures that
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every training
case will keep getting picked
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If
the output is correct, leave its weights alone.
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If
the output is 0 but should be 1, add the feature
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vector
to the weight vector.
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If
the output is 1 but should be 0, subtract the feature
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vector
from the weight vector
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This is
guaranteed to find a set of weights that gets the
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right answer on
the whole training set if any such
set exists
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There is no need
to choose a learning rate.
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