Modelling the extrapolation ability of multi-layer pereeptrons

被引：0
|
作者：
Browne, A [1 ]
机构：
[1] London Guildhall Univ, Sch Comp Informat Syst & Math, London EC3N 1JY, England
来源：
6TH WORLD MULTICONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL V, PROCEEDINGS: COMPUTER SCI I | 2002年
关键词：
neural networks; extrapolation; distributed representation; localist representation; identity task; circular convolution;
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：
To give a good explanation of human cognition and perform certain practical tasks connectionist systems must be able to extrapolate. This paper explores the relationship between the input representation used for a task and the extrapolation performance of an MLP trained on that task. It has been discovered that representation has a marked effect on the extrapolation properties of MLPs when trained to do the identity task.
引用
收藏
页码：429 / 433
页数：5
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