An effIcient pruning algorithm for sparselized higher-order neural networks

被引：0
|
作者：
Wang, YB [1 ]
Li, TX [1 ]
Li, AY [1 ]
Li, WC [1 ]
机构：
[1] Commun Univ China, Comp & Software Sch, Beijing, Peoples R China
来源：
ICAI '05: PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2 | 2005年
关键词：
higher-order neural networks; redundant connection weights; sparselized higher-order neural networks; pruning algorithm; sunspots series;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An efficient pruning algorithm for sparselized higher-older neural networks is proposed in this paper Using the algorithm a smallest architecture of higher-order neural network can be generated easily. To evaluate the good generalization ability an example of activity of sunspots series has been simulated and provided. As an important parameter VC dimension (Vapnik-Chervonenkis dimension) was applied for designing the training set. Simulation results are included to illustrate the effectiveness of the pruning algorithm and the good generalization ability of the sparselized higher-order neural network.
引用
收藏
页码：189 / 194
页数：6
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