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The “Bottleneck” Behaviours in Linear Feedforward Neural Network Classifiers and Their Breakthrough 被引量:2
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作者 黄德双 《Journal of Computer Science & Technology》 SCIE EI CSCD 1999年第1期34-43,共10页
The classification mechanisms of linear feedforward neural network classifiers (FNNC), whose hidden layer performs the Fisher linear transformation of the input patterns, under the supervision of outer-supervised sign... The classification mechanisms of linear feedforward neural network classifiers (FNNC), whose hidden layer performs the Fisher linear transformation of the input patterns, under the supervision of outer-supervised signals are inves- tigated. The 'bottleneck' behaviours in linear FNNCs are observed and analyzed. In addition, the structure stabilities of the linear FNNCs are also discussed. It is pointed out that the key point to break through the 'bottleneck' behaviours for lin- ear FNNCs is to change linear hidden neurons into nonlinear hidden ones. Finally, the experimental results, taking the parity 3 problem as example, are given. 展开更多
关键词 feedforward neural network mean square classifiers outer-supervised signal classification
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