Learning and generalization theories of large committee--machines

Remi Monasson; Riccardo Zecchina

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Learning and generalization theories of large committee--machines

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arxiv cond-mat/9601122 v1 pith:X4KRUSFW submitted 1996-01-25 cond-mat

Learning and generalization theories of large committee--machines

Remi Monasson , Riccardo Zecchina This is my paper

classification cond-mat

keywords largelearningalphageneralizationallowsassociatedbayesiancapacity

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abstract

The study of the distribution of volumes associated to the internal representations of learning examples allows us to derive the critical learning capacity ($\alpha_c=\frac{16}{\pi} \sqrt{\ln K}$) 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 $\alpha=K$.

Learning and generalization theories of large committee--machines

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