The certainty factor-based neural network in continuous classification domains

被引：0

作者：

Fu, LM ^{[1
]}

机构：

[1] Univ Florida, Dept Comp & Informat Sci, Gainesville, FL 32611 USA

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2000年 / 30卷 / 04期

基金：

美国国家科学基金会;

关键词：

certainty factor; classification; fuzzy set theory; machine learning; neural network; probability estimation;

D O I：

10.1109/3477.865176

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The integration of certainty factors (CFs) into the neural computing framework has resulted in a special artificial neural network known as the CFNet. This paper presents the cont-CFNet, which is devoted to classification domains where instances are described by continuous attributes. A new mathematical analysis on Learning behavior, specifically linear versus nonlinear learning, is provided that can serve to explain how the cont-CFNet discovers patterns and estimates output probabilities. Its advantages in performance and speed have been demonstrated in empirical studies.

引用

页码：581 / 586

页数：6

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