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| • |
Consider the
squared
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distance between
any
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satisfactory
weight vector
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and the current
weight
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vector.
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– |
Every
time the
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perceptron
makes a
|
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|
mistake,
the learning
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algorithm
reduces the
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squared
distance
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between
the current
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weight
vector and any
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satisfactory
weight
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vector
(unless it crosses
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the
decision plane).
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| • |
So consider
“generously satisfactory”
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weight vectors
that lie within the
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feasible region
by a margin at least as
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great as the
largest update.
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– |
Every
time the perceptron makes a
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|
mistake,
the squared distance to all
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of
these weight vectors is always
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decreased
by at least the squared
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length
of the smallest update vector.
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