Fuzzy-Pattern-Classifier Training with Small Data Sets

被引：0
|
作者：
Moenks, Uwe [1 ]
Petker, Denis [2 ]
Lohweg, Volker [1 ]
机构：
[1] Ostwestfalen Lippe Univ Appl Sci, InIT Inst Ind IT, Liebigstr 87, D-32657 Lemgo, Germany
[2] OWITA GmbH, D-32657 Lemgo, Germany
来源：
INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: THEORY AND METHODS, PT 1 | 2010年 / 80卷
关键词：
Fuzzy Logic; Probability Theory; Fuzzy-Pattern-Classification; Machine Learning; Artificial Intelligence; Pattern Recognition;
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：
It is likely in real-world applications that only little data is available for training a knowledge-based system. We present a method for automatically training the knowledge-representing membership functions of a Fuzzy-Pattern-Classification system that works also when only little data is available and the universal set is described insufficiently. Actually, this paper presents how the Modified-Fuzzy-Pattern-Classifier's membership functions are trained using probability distribution functions.
引用
收藏
页码：426 / +
页数：2
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