Trends in extreme learning machines: A review

Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the...

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Vydané v:	Neural networks Ročník 61; s. 32 - 48
Hlavní autori:	Huang, Gao, Huang, Guang-Bin, Song, Shiji, You, Keyou
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Ltd 01.01.2015
Predmet:	Algorithms Artificial Intelligence - classification Artificial Intelligence - standards Artificial Intelligence - trends Classification Clustering Extreme learning machine Feature learning Regression Feature learning Regression Extreme learning machine Clustering Classification
ISSN:	0893-6080, 1879-2782, 1879-2782
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Abstract	Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives.
AbstractList	Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives. Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives.Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives.
Author	Huang, Gao You, Keyou Huang, Guang-Bin Song, Shiji
Author_xml	– sequence: 1 givenname: Gao orcidid: 0000-0002-7251-0988 surname: Huang fullname: Huang, Gao email: huang-g09@mails.tsinghua.edu.cn organization: Department of Automation, Tsinghua University, Beijing 100084, China – sequence: 2 givenname: Guang-Bin orcidid: 0000-0002-2480-4965 surname: Huang fullname: Huang, Guang-Bin email: egbhuang@ntu.edu.sg organization: School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore – sequence: 3 givenname: Shiji surname: Song fullname: Song, Shiji email: shijis@mail.tsinghua.edu.cn organization: Department of Automation, Tsinghua University, Beijing 100084, China – sequence: 4 givenname: Keyou orcidid: 0000-0003-4355-5340 surname: You fullname: You, Keyou email: youky@mail.tsinghua.edu.cn organization: Department of Automation, Tsinghua University, Beijing 100084, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/25462632$$D View this record in MEDLINE/PubMed
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Snippet	Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the...
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SubjectTerms	Algorithms Artificial Intelligence - classification Artificial Intelligence - standards Artificial Intelligence - trends Classification Clustering Extreme learning machine Feature learning Regression
Title	Trends in extreme learning machines: A review
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