Fuzzy classification by fuzzy labeled neural gas

被引：22
作者：
Villmann, Th.
Hammer, B.
Schleif, F.
Geweniger, T.
Herrmann, W.
机构：
[1] Univ Leipzig, Clin Psychotherapy, D-04107 Leipzig, Germany
[2] Tech Univ Clausthal, Dept Math & Comp Sci, Clausthal Zellerfeld, Germany
[3] Univ Leipzig, Dept Comp Sci, D-04107 Leipzig, Germany
[4] Bruker Dalton GmbH Leipzig, Leipzig, Germany
来源：
NEURAL NETWORKS | 2006年 / 19卷 / 6-7期
关键词：
learning vector quantization; relevance learning; metric adaptation; classification;
D O I：
10.1016/j.neunet.2006.05.026
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We extend the neural gas for supervised fuzzy classification. In this way we are able to learn crisp as well as fuzzy clustering, given labeled data. Based on the neural gas cost function, we propose three different ways to incorporate the additional class information into the learning algorithm. We demonstrate the effect on the location of the prototypes and the classification accuracy. Further, we show that relevance learning can be easily included. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码：772 / 779
页数：8
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