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Target Coding for Extreme Learning Machine

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Proceedings of ELM-2017 (ELM 2017)

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 10))

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Abstract

Target coding is an indispensable part of supervised learning. Currently, the assumption of the most popular target coding like one-hot (one-of-K) coding is that targets are independent. However, this assumption is limited due to the complex relationship between targets. In this paper, we will explore the effects of kinds of target coding methods on the performance of Extreme Learning Machine Classifiers (ELM-C). Linearly independent coding (e.g., one-of-k coding, Hadamard coding) and linearly dependent coding (e.g., ordinal coding, binary coding) are analyzed and compared. The experimental results on OCR letter dataset show that different target coding will indeed affect the performance of the same classifier.

D. Cui—This work is supported by Delta Joint Lab.

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Author information

Authors and Affiliations

  1. Energy Research Institute @ NTU (ERI@N), Interdisciplinary Graduate School, Singapore, Singapore

    Dongshun Cui

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Dongshun Cui, Guanghao Zhang, Wei Han & Guang-Bin Huang

  3. College of Information Engineering, Xiangtan University, Xiangtan, China

    Kai Hu

Authors
  1. Dongshun Cui
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  2. Kai Hu
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  3. Guanghao Zhang
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  4. Wei Han
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  5. Guang-Bin Huang
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Corresponding author

Correspondence to Dongshun Cui .

Editor information

Editors and Affiliations

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

    Jiuwen Cao

  2. Department of Computer and Information Science, University of Macau, Macau, China

    Chi Man Vong

  3. Nokia Bell Labs, Espoo, Finland

    Yoan Miche

  4. Department of Information and Logistics, College of Technology at the University of Houston, Houston, TX, USA

    Amaury Lendasse

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Cui, D., Hu, K., Zhang, G., Han, W., Huang, GB. (2019). Target Coding for Extreme Learning Machine. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM-2017. ELM 2017. Proceedings in Adaptation, Learning and Optimization, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-01520-6_27

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