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“Perceptrons”
describes a whole family of learning
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machines, but
the standard type consisted of a layer of
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fixed non-linear
basis functions followed by a simple
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linear
discriminant function.
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– |
They
were introduced in the late 1950’s and they had
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a
simple online learning procedure.
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Grand
claims were made about their abilities. This led
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to
lots of controversy.
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Researchers
in symbolic AI emphasized their
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limitations
(as part of an ideological campaign against
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real
numbers, probabilities, and learning)
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Support Vector
Machines are just perceptrons with a
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clever way of
choosing the non-adaptive, non-linear
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basis functions
and a better learning procedure.
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They
have all the same limitations as perceptrons in
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what
types of function they can learn.
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But
people seem to have forgotten this.
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