On-line learning in a discrete state space

W. Kinzel; R. Urbanczik

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arXiv · cond-mat/9705257

On-line learning in a discrete state space

W. Kinzel·R. Urbanczik

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Abstract

On-line learning of a rule given by an N-dimensional Ising perceptron, is considered for the case when the student is constrained to take values in a discrete state space of size $L^N$. For L=2 no on-line algorithm can achieve a finite overlap with the teacher in the thermodynamic limit. However, if $L$ is on the order of $\sqrt{N}$, Hebbian learning does achieve a finite overlap.

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BibTeX RIS

W. Kinzel, R. Urbanczik. 1997-05-26. On-line learning in a discrete state space. https://arxiv.org/abs/cond-mat/9705257

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