极限学习机
计算机科学
数据流挖掘
人工智能
分类器(UML)
人工神经网络
机器学习
前馈
二进制数
二元分类
数据流
前馈神经网络
建筑
支持向量机
工程类
数学
算术
艺术
电信
控制工程
视觉艺术
作者
Karol Wojtachnia,Joanna Komorniczak,Paweł Ksieniewicz
出处
期刊:Lecture notes in networks and systems
日期:2023-01-01
卷期号:: 35-44
标识
DOI:10.1007/978-3-031-41630-9_4
摘要
Classifier ensembles have shown the ability to classify drifted data streams. The following paper proposes an ensemble consisting of a single hidden layer feedforward neural network and an Extreme Learning Machine. For this purpose, a new incremental version of the Extreme Learning Machine is also proposed. Motivations behind such an approach have been precisely described and supported by conducted research. The achieved results show when the architecture might be most useful and what are the possible directions for future development of this method.
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