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Vapnik and his
co-workers developed a very clever type
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of perceptron
called a Support Vector Machine.
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Instead
of hand-coding the layer of non-adaptive
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features,
each training example is used to create a
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new
feature using a fixed recipe.
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The
feature computes how similar a test example is to that
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training
example.
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Then
a clever optimization technique is used to select
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the
best subset of the features and to decide how to
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weight
each feature when classifying a test case.
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But its
just a perceptron and has all the same limitations.
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In the 1990’s,
many researchers abandoned neural
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networks with
multiple adaptive hidden layers because
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Support Vector
Machines worked better.
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