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[Author] Kazuki SESHIMO(1hit)

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  • Practical Evaluation of Online Heterogeneous Machine Learning

    Kazuki SESHIMO  Akira OTA  Daichi NISHIO  Satoshi YAMANE  

     
    PAPER-Artificial Intelligence, Data Mining

      Pubricized:
    2020/08/31
      Vol:
    E103-D No:12
      Page(s):
    2620-2631

    In recent years, the use of big data has attracted more attention, and many techniques for data analysis have been proposed. Big data analysis is difficult, however, because such data varies greatly in its regularity. Heterogeneous mixture machine learning is one algorithm for analyzing such data efficiently. In this study, we propose online heterogeneous learning based on an online EM algorithm. Experiments show that this algorithm has higher learning accuracy than that of a conventional method and is practical. The online learning approach will make this algorithm useful in the field of data analysis.