The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Ces dernières années, l’utilisation du Big Data a attiré davantage d’attention et de nombreuses techniques d’analyse des données ont été proposées. L’analyse des mégadonnées est toutefois difficile, car la régularité de ces données varie considérablement. L’apprentissage automatique des mélanges hétérogènes est un algorithme permettant d’analyser efficacement ces données. Dans cette étude, nous proposons un apprentissage hétérogène en ligne basé sur un algorithme EM en ligne. Les expériences montrent que cet algorithme a une précision d’apprentissage supérieure à celle d’une méthode conventionnelle et qu’il est pratique. L'approche d'apprentissage en ligne rendra cet algorithme utile dans le domaine de l'analyse de données.
Kazuki SESHIMO
Kanazawa University
Akira OTA
Kanazawa University
Daichi NISHIO
Kanazawa University
Satoshi YAMANE
Kanazawa University
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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},}
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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 -