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Directed and
undirected models seem very different.
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But there is a
special type of multi-layer directed model
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in which it is
easy to infer the posterior distribution over
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the hidden units
because it has complementary
priors.
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This special
type of directed model is equivalent to an
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undirected model.
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At
first, this equivalence just seems like a neat trick
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But
it leads to a very effective new learning algorithm
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that
allows multilayer directed nets to be learned one
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layer
at a time.
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The new
learning algorithm resembles boosting with each
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layer
being like a weak learner.
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