Learning and generalization theories of large committee--machines

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The study of the distribution of volumes associated to the internal representations of learning examples allows us to derive the critical learning capacity ($α_c=\frac{16}π \sqrt{\ln K}$) of large committee machines, to verify the stability of the solution in the limit of a large number $K$ of hidden units and to find a Bayesian generalization cross--over at $α=K$.
14 pages, revtex

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