| 11 |
| A very surprising fact |
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| • | Everything that
one weight needs to know about |
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| the other weights
and the data in order to do |
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| maximum
likelihood learning is contained in the |
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| difference of two
correlations. |
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| Expected
value of |
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| product
of states at |
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| thermal
equilibrium |
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| when the
training |
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| vector
is clamped |
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| on
the visible units |
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| Expected
value of |
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| product
of states at |
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| thermal
equilibrium |
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| when
nothing is |
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| clamped |
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| Derivative
of |
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| log
probability |
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| of
one training |
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| vector |
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