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Abstract:
The learning process in Boltzmann Machines is computationally
intractible. We present a new approximate learning algorithm for
Boltzmann Machines, which is based on mean field theory and the
linear response theorem. The computational complexity of the
algorithm is cubic in the number of neurons.
In the absence of hidden units, we show how the weights can be
directly computed from the fixed point equation of the learning
rules. We show that the solution of this method is close to optimal
and gives a significant improvement over the naive mean field
approach.
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