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.

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

Corresponding author

Correspondence toJana Backhus.

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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