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Each training
case consists of an input vector x and a
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desired output y
(there may be multiple desired outputs
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but we will
ignore that for now)
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Regression: Desired output is a real number
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Classification: Desired output is a class label (1 or 0
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is
the simplest case).
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We start by
choosing a model-class
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A
model-class is a way of using some numerical
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parameters, W, to map
each input vector, x, into a
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predicted
output y
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Learning usually
means adjusting the parameters to
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reduce the
discrepancy between the desired output on
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each training
case and the actual output produced by
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the model.
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