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 |
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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
|
|
|
mistake,
the learning
|
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|
algorithm
moves the
|
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|
current
weight vector
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towards
all satisfactory
|
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|
weight
vectors (unless it
|
|
crosses
the constraint
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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
|
|
|
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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|
