GENERALIZATION IN A HOPFIELD NETWORK
GENERALIZATION IN A HOPFIELD NETWORK
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DOI：
10.1051/jphys:0199000510210242100
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发表时间：
1990-11-01
期刊：
影响因子：
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通讯作者：
FONTANARI, JF
中科院分区：
文献类型：
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作者：
FONTANARI, JF
研究了 Hopfield 网络在学习大量概念时的性能，该网络只能访问例证概念的有限供应的典型数据。为了开始创建概念的表示，必须向网络教授的最小示例数量是通过分析计算的。结果表明，混合态在这些表示的创建中起着至关重要的作用。
The performance of a Hopfield network in learning an extensive number of concepts having access only to a finite supply of typical data which exemplify the concepts is studied. The minimal number of examples which must be taught to the network in order it starts to create representations for the concepts is calculated analitically. It is shown that the mixture states play a crucial role in the creation of these representations.