Comparison Study on Artificial Neural Network and Online Sequential Extreme Learning Machine in Regression Problem

At present, machine learning techniques is frequently used to extraction and/or prediction some information from enormous collected data. Traditional machine learning is performed batch learning like a popular one, Artificial Neural Network (ANN). That means it can't integrate new information i...

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Bibliographic Details
Published in2019 7th International Electrical Engineering Congress (iEECON) pp. 1 - 4
Main Authors Chupong, Charnon, Plangklang, Boonyang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2019
Subjects
artificial neural network
Artificial neural networks
Data models
extreme learning machine
Machine learning
online incremental learning
Predictive models
Training
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Summary:At present, machine learning techniques is frequently used to extraction and/or prediction some information from enormous collected data. Traditional machine learning is performed batch learning like a popular one, Artificial Neural Network (ANN). That means it can't integrate new information into already trained model but it is retrained from scratch. Online Sequential Extreme Learning Machine (OS-ELM) is a one of online incremental machine learning techniques that can learn and update model from new receive data without to retrain the model. This paper shows the result of comparison experiment between ANN and OS-ELM and found that, the OS-ELM has acceptable performance in situation of few initial data for training and/or statistic properties of data is changing while it working.
DOI:10.1109/iEECON45304.2019.8938990