标题: PARALLEL PERCEPTRON LEARNING ON A SINGLE-CHANNEL BROADCAST COMMUNICATION MODEL
作者: HONG, TP
TSENG, SS
资讯科学与工程研究所
Institute of Computer Science and Engineering
关键字: PERCEPTRON;SEPARABLE;PARALLEL LEARNING;BROADCAST COMMUNICATION MODEL;BACKPROPAGATION
公开日期: 1-二月-1992
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 ot 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). Extensions to a bounded number of processors and to the backpropagation learning have also been discussed.
URI: http://hdl.handle.net/11536/3545
ISSN: 0167-8191
期刊: PARALLEL COMPUTING
Volume: 18
Issue: 2
起始页: 133
结束页: 148
显示于类别:Articles