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
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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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页码:419 / 424
页数:6
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