An optimal parallel perceptron learning
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Tzung-Pei Hong; Shian-Shyong Tseng
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Article
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1994
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Elsevier Science
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English
β 211 KB
In [2], a parallel perceptron learning algorithm on the single-channel broadcast communication model was proposed to speed up the learning of weights of perceptrons [3]. The results in [2] showed that given n training examples, the average speedup is 1.48\*n~ n by n processors. Here, we explain how