Journal ArticleDOI
Extreme learning machines: a survey
TLDR
A survey on Extreme learning machine (ELM) and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELm, (3) online sequential ELM; and (4) incremental ELM and (5) ensemble ofELM.Abstract:
Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational scalability. Extreme learning machine (ELM) as emergent technology which overcomes some challenges faced by other techniques has recently attracted the attention from more and more researchers. ELM works for generalized single-hidden layer feedforward networks (SLFNs). The essence of ELM is that the hidden layer of SLFNs need not be tuned. Compared with those traditional computational intelligence techniques, ELM provides better generalization performance at a much faster learning speed and with least human intervene. This paper gives a survey on ELM and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELM, (3) online sequential ELM, (4) incremental ELM, and (5) ensemble of ELM.read more
Citations
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Journal ArticleDOI
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Anna L. Buczak,Erhan Guven +1 more
TL;DR: The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/ DM for cyber security is presented, and some recommendations on when to use a given method are provided.
Journal ArticleDOI
Trends in extreme learning machines
TL;DR: In this paper, the authors report the current state of the theoretical research and practical advances on this subject and provide a comprehensive view of these advances in ELM together with its future perspectives.
Book
Extreme Learning Machine
Erik Cambria,Guang-Bin Huang,Liyanaarachchi Lekamalage Chamara Kasun,Hongming Zhou,Chi-Man Vong,Jiarun Lin,Jianping Yin,Zhiping Cai,Qiang Liu,Kuan Li,Victor C. M. Leung,Liang Feng,Yew-Soon Ong,Meng-Hiot Lim,Anton Akusok,Amaury Lendasse,Francesco Corona,Rui Nian,Yoan Miche,Paolo Gastaldo,Rodolfo Zunino,Sergio Decherchi,Xuefeng Yang,Kezhi Mao,Beom-Seok Oh,Jehyoung Jeon,Kar-Ann Toh,Andrew Beng Jin Teoh,Jaihie Kim,Hanchao Yu,Yiqiang Chen,Junfa Liu +31 more
TL;DR: This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation.
Journal ArticleDOI
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
TL;DR: The study of Baesens et al. (2003) is updated and several novel classification algorithms to the state-of-the-art in credit scoring are compared, providing an independent assessment of recent scoring methods and offering a new baseline to which future approaches can be compared.
Journal ArticleDOI
A systematic literature review of machine learning methods applied to predictive maintenance
Thyago Peres Carvalho,Fabrizzio Soares,Fabrizzio Soares,Roberto Vita,Roberto da Piedade Francisco,Joao Pedro Tavares Vieira Basto,Symone Gomes Soares Alcalá +6 more
TL;DR: A systematic literature review of ML methods applied to PdM, showing which are being explored in this field and the performance of the current state-of-the-art ML techniques.
References
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