Parallel strategy for optimal learning in perceptrons
نویسنده
چکیده
Abstract. We developed a parallel strategy for learning optimally specific realizable rules by perceptrons, in an on-line learning scenario. Our result is a generalisation of the Caticha-Kinouchi (CK) algorithm developed for learning a perceptron with a synaptic vector drawn from a uniform distribution over the N -dimensional sphere, so called the typical case. Our method outperforms the CK algorithm in almost all possible situations, failing only in a denumerable set of cases. The algorithm is optimal in the sense that it saturates Bayesian bounds when it succeeds.
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