Parallel perceptron learning on a single-channel broadcast communication model

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題名:	Parallel perceptron learning on a single-channel broadcast communication model
作者:	T. P. Hong;S. S. Tseng
貢獻者:	Department of Information Science and Applications
關鍵詞:	Perceptron;separable;parallel learning;broadcast communication model;backpropagation
日期:	1992
上傳時間:	2009-11-30 08:03:05 (UTC+0)
出版者:	Asia University
摘要:	A parallel perceptron learning algorithm based upon a single-channel broadcast communication model has been proposed here. Since it can process training instances in parallel, instead of one by one in the conventional algorithm, large speedup can be expected. Theoretical analysis shows: with n processors, the average speedup ranges from O(log n) to O(n) under a variety of assumptions (where n is the number of training instances). Experimental results further show the actual average speedup is approximately being O(n0.91/log n). Extension to a bounded number of processors and to the backpropagtion learning have also been discussed.
關聯:	Parallel Computing 18(2):133-148
顯示於類別:	[行動商務與多媒體應用學系] 期刊論文

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