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

页码：426 / +

页数：2

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