Learning the reversed-wedge problem using a multi-interacting perceptron with correlated weights

E Botelho

Physica A: Statistical Mechanics and its Applications, 1998, vol. 253, issue 1, 323-332

Abstract: We consider the task of learning the so-called reversed-wedge problem, using a multi-interacting perceptron with first- and third-order synap-ses, where the third-order synaptic couplings are expressed as products of the first-order synapses associated to the neurons involved in the corresponding multi-interaction. This correlation condition allows the training of the multi-interacting perceptron to be achieved by adjusting the set of first-order weights, in such a way that the learning rates scales with the dimensionality of a simple perceptron. Remarkably, if the width of the “reversed” inner region (wedge) is smaller than 23, the high-temperature approach predicts a transition from a poor generalization regime to a state with good performance, where the generalization error is identical to the results for the problem of a simple perceptron learning a linearly separable rule. The simulation results are in excellent agreement with the analytical predictions.

Keywords: Reversed-wedge problem; Multi-interacting perceptrons (search for similar items in EconPapers)
Date: 1998
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:253:y:1998:i:1:p:323-332

DOI: 10.1016/S0378-4371(97)00657-2

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