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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 94  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
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
14:15
Okayama Okayama University of Science
(Primary: On-site, Secondary: Online)
A Study on Multilevel Coefficient Linear Regression Model and an Optimal Prediction for Multilevel Data by Bayes Decision Theory
Kohei Horinouchi, Naoki Ichijo, Taisuke Ishiwatari, Toshiyasu Matsushima (Waseda Univ.) IT2022-26
It is common practice to apply Multilevel Model (Linear Mixed Model, Hierarchical Linear Model) for the data sampled fro... [more] IT2022-26
pp.56-60
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
IT, EMM 2022-05-17
13:25
Gifu Gifu University
(Primary: On-site, Secondary: Online)
A Note on Time-Varying Two-Dimensional Autoregressive Models and the Bayes Codes
Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2022-2 EMM2022-2
This paper proposes a two-dimensional autoregressive model with time-varying parameters as a stochastic model for explai... [more] IT2022-2 EMM2022-2
pp.7-12
IBISML 2022-03-08
11:20
Online Online Tree-Structured Generative Model with Latent Variables and Approximate Variational Bayesian Inference
Naoki Ichijo, Yuta Nakahara (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IBISML2021-33
 [more] IBISML2021-33
pp.19-26
RCS, SIP, IT 2022-01-21
09:00
Online Online An Approximation by Meta-Tree Boosting Method to Bayesian Optimal Prediction for Decision Tree Model
Wenbin Yu, Koki Kazama, Yuta Nakahara, Naoki Ichijo (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IT2021-67 SIP2021-75 RCS2021-235
 [more] IT2021-67 SIP2021-75 RCS2021-235
pp.219-224
IT 2021-07-09
13:00
Online Online Bayesian Optimal Prediction and Its Approximation Algorithm for the Difference of Response Variables with and without Measures Considering Individual Differences by Assuming Latent Clusters
Taisuke Ishiwatari (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IT2021-23
In observational studies, there are problems such as "the measure can be given only once to the target" and "the charact... [more] IT2021-23
pp.45-50
IT 2021-07-09
13:25
Online Online A Note on the Reduction of Computational Complexity for Linear Regression Model Including Cluster Explanatory Variables and Regression Explanatory Variables -- Bayes Optimal Prediction and Sub-Optimal Algorithm --
Sho Kayama (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IT2021-24
By considering the probability model with the structure that the data is divided into clusters and each cluster has an i... [more] IT2021-24
pp.51-56
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
WBS, IT, ISEC 2021-03-04
13:20
Online Online [Poster Presentation] Non-asymptotic converse theorem on the overflow probability of variable-to-fixed length codes
Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-130 ISEC2020-60 WBS2020-49
This study considers variable-to-fixed length codes and investigates the non-asymptotic converse theorem on the threshol... [more] IT2020-130 ISEC2020-60 WBS2020-49
pp.115-116
EA, US, SP, SIP, IPSJ-SLP [detail] 2021-03-03
16:45
Online Online An optimal prediction of phoneme under Bayes criterion by weighting multiple hidden Markov models
Taishi Yamaoka, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) EA2020-76 SIP2020-107 SP2020-41
In this paper, we propose a prediction method for prediction problems using a hidden Markov model. Specifically, it is a... [more] EA2020-76 SIP2020-107 SP2020-41
pp.97-102
IBISML 2021-03-03
14:25
Online Online Markov Decision Processes for Simultaneous Control of Multiple Objects with Different State Transition Probabilities in Each Cluster
Yuto Motomura, Akira Kamatsuka, Koki Kazama, Toshiyasu Matsushima (Waseda Univ.) IBISML2020-49
In this study, we propose an extended MDP model, which is a Markov decision process model with multiple control objects ... [more] IBISML2020-49
pp.47-54
SIP, IT, RCS 2021-01-22
15:15
Online Online An Image Generative Model with Various Auto-regressive Coefficients Depending on Neighboring Pixels and the Bayes Code for It
Masahiro Takano, Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2020-108 SIP2020-86 RCS2020-199
In this papar, we propose an expanded model of an autoregressive stochastic generative model for images. This model cont... [more] IT2020-108 SIP2020-86 RCS2020-199
pp.253-258
IT 2020-12-02
09:20
Online Online Performance Limit of Classification in the Presence of Label Noise with Erasure
Goki Yasuda, Tota Suko, Manabu Kobayashi, Toshiyasu Matsushima (Waseda Univ.) IT2020-29
 [more] IT2020-29
pp.26-31
IT 2020-12-02
09:40
Online Online Approximation Method for Bayes Optimal Prediction in Phoneme Recognition Problem
Taishi Yamaoka, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-30
In this paper, we propose a method of phoneme recognition. In the previous studies on phoneme recognition using the Hidd... [more] IT2020-30
pp.32-37
IT 2020-12-02
10:00
Online Online Policy Optimization Based on Bayesian Decision Theory in Learning Period on Markov Decision Process
Naoki Ichijo, Yuta Nakahara, Yuto Motomura, Toshiyasu Matsushima (Waseda Univ.) IT2020-31
In Markov decision process(MDP) problems with an unknown transition probability, a learning agent has to learn the unkno... [more] IT2020-31
pp.38-43
IT 2020-12-02
10:30
Online Online Error Probability of Classification Based on the Analysis of the Bayes Code -- Extension and Example --
Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-32
Suppose that we have two training sequences generated by parametrized distributions $P_{theta^*}$ and $P_{xi^*}$, where ... [more] IT2020-32
pp.44-49
IT 2020-07-16
14:45
Online Online Asymptotic Evaluation of $alpha$-divergence between VB Posterior Predictive Distribution and Bayesian Predictive Distribution
Kazuki Yamada, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-14
In this paper, we consider the problem of determining probability distribution of $X_{n+1}$ given ${X_i }_{i=1}^{n}$ fol... [more] IT2020-14
pp.19-23
IT, EMM 2020-05-28
15:00
Online Online Bayes Optimal Detecting Relevant Changes for i.p.i.d. Sources
Kairi Suzuki, Akira Kamatsuka, Toshiyasu Matsushima (Waseda Univ.) IT2020-3 EMM2020-3
The problems of detecting change points are studied in various fields.
There are various types of change-point detectio... [more]
IT2020-3 EMM2020-3
pp.13-18
 Results 1 - 20 of 94  /  [Next]  
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