A nearest-neighbor method with self-organizing incremental neural network

被引：1

作者：

Furao, Shen ^{[1
]}

Hasegawa, Osamu ^{[1
]}

机构：

[1] Nanjing Univ, Key Lab Novel Software Technol, Nanjing 210093, Peoples R China

来源：

2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6 | 2007年

关键词：

D O I：

10.1109/IJCNN.2007.4371119

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce a prototype-based nearest-neighbor method that is based on a self-organizing incremental neural network (SOINN). It automatically learns the number of prototypes necessary to determine the decision boundary, and it Is robust to noisy training data. The experiments with artificial datasets and real-world datasets illustrate the efficiency of the proposed method.

引用

页码：1145 / 1150

页数：6

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