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Online EM for the Normalized Gaussian Network with Weight-Time-Dependent Updates

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9950))

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Abstract

In this paper, we propose a weight-time-dependent (WTD) update approach for an online EM algorithm applied to the Normalized Gaussian network (NGnet). WTD aims to improve a recently proposed weight-dependent (WD) update approach by Celaya and Agostini. First, we discuss the derivation of WD from an older time-dependent (TD) update approach. Then, we consider additional aspects to improve WD, and by including them we derive the new WTD approach from TD. The difference between WD and WTD is discussed, and some experiments are conducted to demonstrate the effectiveness of the proposed approach. WTD succeeds in improving the learning performance for a function approximation task with balanced and dynamic data distributions.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Technology, Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Kita-ku, Sapporo, 060-0814, Japan

    Jana Backhus, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo & Masanori Sugimoto

  2. JST PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan

    Ichigaku Takigawa

Authors
  1. Jana Backhus
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  2. Ichigaku Takigawa
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  3. Hideyuki Imai
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  4. Mineichi Kudo
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  5. Masanori Sugimoto
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Corresponding author

Correspondence to Jana Backhus .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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© 2016 Springer International Publishing AG

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Backhus, J., Takigawa, I., Imai, H., Kudo, M., Sugimoto, M. (2016). Online EM for the Normalized Gaussian Network with Weight-Time-Dependent Updates. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_64

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  • DOI: https://doi.org/10.1007/978-3-319-46681-1_64

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46680-4

  • Online ISBN: 978-3-319-46681-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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