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Instead of
trying to predict the answer directly from the
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raw inputs we
could start by extracting a layer of
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“features”.
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Sensible
if we already know that certain combinations
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of
input values would be useful (e.g. edges or corners
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in
an image).
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Instead of
learning the features we could design them by
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hand.
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The
hand-coded features are equivalent to a layer of
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non-linear
neurons that do not need to be learned.
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If
we use a very big set of features for a two-class
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problem,
the classes will almost certainly be linearly
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separable.
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But
surely the linear separator will give poor generalization.
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