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Multi-layer Online Sequential Extreme Learning Machine for Image Classification

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Proceedings of ELM-2015 Volume 1

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 6))

Abstract

In this paper, a multi-layer online sequential extreme learning machine (ML-OSELM) is proposed for image classification. ML-OSELM is an online sequential version of a recently proposed multi-layer extreme learning machine (ML-ELM) method for batch learning. Existing ELM-based sequential learning methods, such as state-of-the-art online sequential extreme learning machine (OS-ELM), were proposed only for single-hidden-layer networks. A distinctive feature of the new method is that it can sequentially train a multi-hidden-layer ELM network. Auto-encoders are used to perform layer-by-layer unsupervised sequential learning in ML-OSELM. We used four image classification datasets in our experiments and ML-OSELM performs better than the OS-ELM method on all of them.

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Authors and Affiliations

  1. Information Systems Technology & Design Pillar, Singapore University of Technology & Design, Singapore, Singapore

    Bilal Mirza, Stanley Kok & Fei Dong

Authors
  1. Bilal Mirza
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  2. Stanley Kok
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  3. Fei Dong
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Corresponding author

Correspondence to Bilal Mirza .

Editor information

Editors and Affiliations

  1. Institute of Information and Contro, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  3. ECE, U of Windsor, WINDSOR, Ontario, Canada

    Jonathan Wu

  4. Dept of Mechanical and Industrial Engg, University of Iowa, Iowa City, Iowa, USA

    Amaury Lendasse

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Mirza, B., Kok, S., Dong, F. (2016). Multi-layer Online Sequential Extreme Learning Machine for Image Classification. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-28397-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-28397-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28396-8

  • Online ISBN: 978-3-319-28397-5

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