IEICE TRANSACTIONS on Information
Practical Evaluation of Online Heterogeneous Machine Learning
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.12 pp.2620-2631
- Publication Date
- 2020/12/01
- Publicized
- 2020/08/31
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDP7020
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Kazuki SESHIMO
Kanazawa University
Akira OTA
Kanazawa University
Daichi NISHIO
Kanazawa University
Satoshi YAMANE
Kanazawa University
Keyword
Latest Issue
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Kazuki SESHIMO, Akira OTA, Daichi NISHIO, Satoshi YAMANE, "Practical Evaluation of Online Heterogeneous Machine Learning" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 12, pp. 2620-2631, December 2020, doi: 10.1587/transinf.2020EDP7020.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7020/_p
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@ARTICLE{e103-d_12_2620,
author={Kazuki SESHIMO, Akira OTA, Daichi NISHIO, Satoshi YAMANE, },
journal={IEICE TRANSACTIONS on Information},
title={Practical Evaluation of Online Heterogeneous Machine Learning},
year={2020},
volume={E103-D},
number={12},
pages={2620-2631},
abstract={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.},
keywords={},
doi={10.1587/transinf.2020EDP7020},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Practical Evaluation of Online Heterogeneous Machine Learning
T2 - IEICE TRANSACTIONS on Information
SP - 2620
EP - 2631
AU - Kazuki SESHIMO
AU - Akira OTA
AU - Daichi NISHIO
AU - Satoshi YAMANE
PY - 2020
DO - 10.1587/transinf.2020EDP7020
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2020
AB - 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.
ER -
English
