Optimal hyperplane classifier based on entropy number bound

被引：0
|
作者：
Tsuda, K [1 ]
机构：
[1] Electrotech Lab, Tsukuba, Ibaraki 3058568, Japan
来源：
NINTH INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS (ICANN99), VOLS 1 AND 2 | 1999年 / 470期
关键词：
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：
Entropy number bound is a capacity measure for learning machines, which is recently proposed by Williamson et. al.[1]. Based on this capacity measure and the structural risk minimization principle, we actually implement an optimal hyperplane classifier. In on-line character recognition experiment using the tangent distance, our method performed better than the conventional optimal hyperplane classifier based on VC dimension.
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收藏
页码：419 / 424
页数：6
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