منابع مشابه
Parallel strategy for optimal learning in perceptrons
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 alg...
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We derive an analytical formula for the generalization error of linear adaptive classifiers trained with weight decay. Analytical and experimental results are then presented to analyze the optimal value of regularization parameters as a function of the training set size.
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We prove a “Tverberg type” multiple intersection theorem. It strengthens the prime case of the original Tverberg theorem from 1966, as well as the topological Tverberg theorem of Bárány et al. (1980), by adding color constraints. It also provides an improved bound for the (topological) colored Tverberg problem of Bárány & Larman (1992) that is tight in the prime case and asymptotically optimal ...
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The cerebellum is a brain structure which has been traditionally devoted to supervised learning. According to this theory, plasticity at the Parallel Fiber (PF) to Purkinje Cell (PC) synapses is guided by the Climbing fibers (CF), which encode an 'error signal'. Purkinje cells have thus been modeled as perceptrons, learning input/output binary associations. At maximal capacity, a perceptron wit...
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Recomi (Repeated correlation matrix inversion) is a polynomially fast algorithm for searching optimally stable solutions of the perceptron learning problem. For random unbiased and biased patterns it is shown that the algorithm is able to find optimal solutions, if any exist, in at worst O(N) floating point operations. Even beyond the critical storage capacity αc the algorithm is able to find l...
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ژورنال
عنوان ژورنال: Physical Review E
سال: 2001
ISSN: 1063-651X,1095-3787
DOI: 10.1103/physreve.64.011915