Performance of a neural binary pattern classifier

Braham R.

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Performance of a neural binary pattern classifier

机译：神经二进制模式分类器的性能

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The paper describes a binary neural network architecture and its performance in pattern classification. The network is called binary because its inputs are binary and its main components are composed of binary neurons. Apart from the usual input and output layers, the network has two 'hidden' layers, called code layer and linear plane, connected in a feedforward structure. The weights of these feedforward connections are also binary. The performance of the network is demonstrated through binary pattern classification experiments. Comparisons with many one- and two-hidden-layer backpropagation networks are included. The proposed network shows superior performance in all the cases that have been studied.

机译：本文描述了一种二进制神经网络架构及其在模式分类中的性能。该网络之所以称为二进制，是因为其输入是二进制的，并且其主要成分是由二进制神经元组成的。除了通常的输入和输出层，网络还具有两个“隐藏”层，称为代码层和线性平面，它们以前馈结构连接。这些前馈连接的权重也是二进制的。通过二进制模式分类实验证明了网络的性能。包括与许多一层和两层反向传播网络的比较。所提出的网络在所有已研究的案例中均显示出优异的性能。

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《IEE proceedings. Part E》 |1995年第2期|P.152-156|共5页
作者
Braham R.;
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正文语种 eng
中图分类计算技术、计算机技术;电工技术;
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Performance of a neural binary pattern classifier

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