.png){kind=icon}
返回
Multi-Level Fuzzy Min-Max Neural Network Classifier
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2013.2275937.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper a multi-level fuzzy min-max neural network classifier (MLF), which is a supervised learning method, is described. MLF uses basic concepts of the fuzzy min-max (FMM) method in a multi-level structure to classify patterns. This method uses separate classifiers with smaller hyperboxes in different levels to classify the samples that are located in overlapping regions. The final output of the network is formed by combining the outputs of these classifiers. MLF is capable of learning nonlinear boundaries with a single pass through the data. According to the obtained results, the MLF method, compared to the other FMM networks, has the highest performance and the lowest sensitivity to maximum size of the hyperbox parameter (theta), with a training accuracy of 100% in most cases.
Keyword:
Classification
fuzzy min-max
hyperbox
machine learning
neural networks
neurofuzzy
neuron
supervised learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
分享收藏Persistent Effectivity of Gas Plasma-Treated, Long Time-Stored Liquid on Epithelial Cell Adhesion Capacity and Membrane Morphology.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏A Network-Based Set Covering Model for Charging Station Location Problem: A Case Study in Norway
PREAI
分享收藏
查看更多内容