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 |