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Use discriminant
functions directly without probabilities:
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Convert
the input vector into one or more real values
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so
that a simple operation (like threshholding) can be
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applied
to get the class.
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The
real values should be chosen to maximize the useable
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information
about the class label that is in the real value.
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Infer
conditional class probabilities:
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Compute
the conditional probability of each class.
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Then
make a decision that minimizes some loss function
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Compare the
probability of the input under separate,
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class-specific,
generative models.
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E.g.
fit a multivariate Gaussian to the input vectors of
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each
class and see which Gaussian makes a test
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data
vector most probable. (Is
this the best bet?)
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