Knowledge Discovery and Self-Organizing State Space Model
Summary :
A hierarchical structure of the statistical models involving the parametric, state space, generalized state space, and self-organizing state space models is explained. It is shown that by considering higher level modeling, it is possible to develop models quite freely and then to extract essential information from data which has been difficult to obtain due to the use of restricted models. It is also shown that by rising the level of the model, the model selection procedure which has been realized with human expertise can be performed automatically and thus the automatic processing of huge time series data becomes realistic. In other words, the hierarchical statistical modeling facilitates both automatic processing of massive time series data and a new method for knowledge discovery.
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- IEICE TRANSACTIONS on Information Vol.E83-D No.1 pp.36-43
- Publication Date
- 2000/01/25
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- Special Section INVITED PAPER (Special Issue on Surveys on Discovery Science)
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Tomoyuki HIGUCHI, Genshiro KITAGAWA, "Knowledge Discovery and Self-Organizing State Space Model" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 1, pp. 36-43, January 2000, doi: .
Abstract: A hierarchical structure of the statistical models involving the parametric, state space, generalized state space, and self-organizing state space models is explained. It is shown that by considering higher level modeling, it is possible to develop models quite freely and then to extract essential information from data which has been difficult to obtain due to the use of restricted models. It is also shown that by rising the level of the model, the model selection procedure which has been realized with human expertise can be performed automatically and thus the automatic processing of huge time series data becomes realistic. In other words, the hierarchical statistical modeling facilitates both automatic processing of massive time series data and a new method for knowledge discovery.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_1_36/_p
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@ARTICLE{e83-d_1_36,
author={Tomoyuki HIGUCHI, Genshiro KITAGAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Knowledge Discovery and Self-Organizing State Space Model},
year={2000},
volume={E83-D},
number={1},
pages={36-43},
abstract={A hierarchical structure of the statistical models involving the parametric, state space, generalized state space, and self-organizing state space models is explained. It is shown that by considering higher level modeling, it is possible to develop models quite freely and then to extract essential information from data which has been difficult to obtain due to the use of restricted models. It is also shown that by rising the level of the model, the model selection procedure which has been realized with human expertise can be performed automatically and thus the automatic processing of huge time series data becomes realistic. In other words, the hierarchical statistical modeling facilitates both automatic processing of massive time series data and a new method for knowledge discovery.},
keywords={},
doi={},
ISSN={},
month={January},}
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TY - JOUR
TI - Knowledge Discovery and Self-Organizing State Space Model
T2 - IEICE TRANSACTIONS on Information
SP - 36
EP - 43
AU - Tomoyuki HIGUCHI
AU - Genshiro KITAGAWA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2000
AB - A hierarchical structure of the statistical models involving the parametric, state space, generalized state space, and self-organizing state space models is explained. It is shown that by considering higher level modeling, it is possible to develop models quite freely and then to extract essential information from data which has been difficult to obtain due to the use of restricted models. It is also shown that by rising the level of the model, the model selection procedure which has been realized with human expertise can be performed automatically and thus the automatic processing of huge time series data becomes realistic. In other words, the hierarchical statistical modeling facilitates both automatic processing of massive time series data and a new method for knowledge discovery.
ER -
English
