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- W2000810397 abstract "The problem of learning from examples in two-layer neural networks is studied within the framework of statistical mechanics. A fully connected committee machine is trained to implement a task which is not linearly separable. The generalization error as a function of the number of training examples per adjustable weight is calculated in the annealed approximation. For both binary and continuous weights we find a first-order transition with a discontinuous drop in the generalization error. The transitions occur due to a specialization of the hidden units from a symmetric state to one with each hidden unit in the student network specialized on a corresponding unit in the teacher network. The symmetric states of poor generalization remain metastable even for large training sets." @default.
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- W2000810397 title "Learning from examples in fully connected committee machines" @default.
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- W2000810397 doi "https://doi.org/10.1088/0305-4470/26/19/024" @default.
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