Submodular neural network is better than modular neural network and support vector machines for personal verification

被引：0
|
作者：
Nagano, T [1 ]
Hirahara, M [1 ]
Eguchi, H [1 ]
机构：
[1] Hosei Univ, Fac Engn, Tokyo 1848584, Japan
来源：
PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4 | 2003年
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：
A sub-modular neural network (SMNN) proposed a few years ago is compared with the usual modular neural network (MNN) and support vector machines (SVM) in terms of pattern recognition performance. Some computer simulation results showed that SMNN was much superior to MNN and SVM as for rejection rates of patterns in unlearned classes under the condition that they gave almost the same recognition rates for patterns in learned classes. These results strongly suggest that SMNN is more suitable for personal verification systems than the other two as such systems require high rejection rate for patterns in unlearned classes.
引用
收藏
页码：2152 / 2156
页数：5
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