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
Nos últimos anos, o uso de big data tem atraído mais atenção e muitas técnicas de análise de dados têm sido propostas. A análise de big data é difícil, no entanto, porque a regularidade desses dados varia muito. O aprendizado de máquina de mistura heterogênea é um algoritmo para analisar esses dados com eficiência. Neste estudo, propomos aprendizagem heterogênea online baseada em um algoritmo EM online. Experimentos mostram que esse algoritmo tem maior precisão de aprendizado do que um método convencional e é prático. A abordagem de aprendizagem online tornará este algoritmo útil no campo da análise de dados.
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 -