Globally optimal on-line learning rules

被引:0
|
作者
Rattray, M [1 ]
Saad, D [1 ]
机构
[1] Aston Univ, Dept Comp Sci & Appl Math, Birmingham B4 7ET, W Midlands, England
关键词
D O I
暂无
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
We present a method for determining the globally optimal on-line learning rule for a soft committee machine. under a statistical mechanics framework. This work complements previous results on locally optimal rules, where only the rate of change in generalization error was considered. We maximize the total reduction in generalization error over the whole learning process and show how the resulting rule can significantly outperform the locally optimal rule.
引用
收藏
页码:322 / 328
页数:7
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