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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
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Committee Date Time Place Paper Title / Authors Abstract Paper #
IT, RCS, SIP 2023-01-25
14:35		 Gunma Maebashi Terrsa
(Primary: On-site, Secondary: Online)		 An Optimal Prediction on Multilevel Coefficient Linear Regression Model by Bayes Decision Theory and Its Approximation Method
Kohei Horinouchi, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.) IT2022-67 SIP2022-118 RCS2022-246		 It is common practice to apply Multilevel Analysis for the data sampled from various classes. In this Analysis, it is co... [more] IT2022-67 SIP2022-118 RCS2022-246
pp.217-222
IT 2022-07-22
13:50		 Okayama Okayama University of Science
(Primary: On-site, Secondary: Online)		 An Efficient Algorithm for Optimal Decision on Piecewise Linear Regression Model by Bayes Decision Theory
Noboru Namegaya, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.) IT2022-25		 In this study, we propose a Beyes-optimal prediction method on a piecewise linear regression model by Bayes decision the... [more] IT2022-25
pp.51-55
IT 2022-07-22
14:40		 Okayama Okayama University of Science
(Primary: On-site, Secondary: Online)		 Meta-Tree Set Construction for Approximate Bayes Optimal Prediction on Decision Tree Model
Keito Tajima, Naoki Ichijo, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.) IT2022-27		 Decision trees are generally used as a predictive function, but some studies use decision trees as data-generative model... [more] IT2022-27
pp.61-66
IT 2022-07-22
15:05		 Okayama Okayama University of Science
(Primary: On-site, Secondary: Online)		 Bayes Optimal Approximation Algorithm by Boosting-like Construction of Meta-Tree Sets in Classification on Decision Tree Model
Ryota Maniwa, Naoki Ichijo, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.) IT2022-28		 Decision trees are used for classification and regression such as predicting the objective variable corresponding to the... [more] IT2022-28
pp.67-72
WBS, IT, ISEC 2021-03-04
10:55		 Online Online An Efficient Bayes Coding Algorithm for the Source Based on Context Tree Models that Vary from Section to Section
Koshi Shimada, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-115 ISEC2020-45 WBS2020-34		 In this paper, we present an efficient coding algorithm for a non-stationary source based on context tree models that ve... [more] IT2020-115 ISEC2020-45 WBS2020-34
pp.19-24
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