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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| ET | 2024-03-02 13:00 |
Miyazaki | Miyazaki University | Verification of Group Characteristics that Promote the Emergent Dialogues in Social Studies Learning Keitaro Tokutake (Titech), Dai Sakuma (Shumei University), Masaki Goto (codeTakt), Masao Murota (Titech)  ET2023-57 |
The purpose of this study is to verify the characteristics of groups that promote emergent dialogue in interactive learn... [more] | ET2023-57 pp.25-31 |
| RECONF | 2020-05-28 15:15 |
Online | Online | RECONF2020-7 |
A Bayesian network is one of the graphical models that represent the causality or correlation of multiple observed pheno... [more] | RECONF2020-7 pp.37-42 |
| NLC, IPSJ-DC | 2018-09-06 17:20 |
Tokyo | Seikei University | Latent co-occurrence words graph extraction using sparse structure estimation
-- Comparison of word vectors between topic model and distributed representation -- Norimitsu Kubono, Nozomi Hiyoshi, Daiju Akashi (PERSOL CAREER) NLC2018-19 |
We are considering application of "structural topic model" in order to extract customer insight from member questionnai... [more] | NLC2018-19 pp.51-56 |
| HCGSYMPO (2nd) |
2017-12-13 - 2017-12-15 |
Ishikawa | THE KANAZAWA THEATRE | Analysis of inattentive state by driving information using sparse structure learning
-- Consideration Based on Difference in Visibility -- Tomoyuki Sakabe, Momoyo Ito (Tokushima Univ.), Kazuhito Sato (Akita Pref. Univ.), Shin-ichi Ito, Minoru Fukumi (Tokushima Univ.) |
The aimless driving is one of the most common cause of the traffic accidents. If we can detect a change of driver's beha... [more] | |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Effect of maximum likelihood estimation after L1 regularization in learning of log-linear models Kazuya Takabatake, Shotaro Akaho (AIST) IBISML2017-86 |
$L_1$ regularization has two functions. One function is the structure learning by parameter reduction, and another func... [more] |
IBISML2017-86 pp.369-375 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Structure Learning of Graph Product Multilayer Network-shaped Gaussian Markov Random Fields Yuya Takashina, Masato Inoue (Waseda Univ.) IBISML2017-88 |
Learning the structure of graphical models is important in many fields, e.g., multivariate analysis and anomaly detectio... [more] | IBISML2017-88 pp.383-388 |
| NLC | 2017-09-08 10:50 |
Tokyo | Seikei University | Applicability of Structural Topic Model to job search site VOC text analysis
-- Feature selection with Bayesian Network Structure Learning -- Norimitsu Kubono, Nozomi Hiyoshi, Daiju Akashi (PERSOL CAREER) NLC2017-25 |
We describe the result of examination applying Structural Topic Model and Bayesian net structure learning complementaril... [more] | NLC2017-25 pp.53-58 |
| PRMU, CNR | 2015-02-20 15:20 |
Miyagi | A Study on Object Tracking with Structured SVM for Indoor Videos Yuki Nagai, Satoshi Ueno, Shigeyuki Sakazawa (KDDI R&D) PRMU2014-150 CNR2014-65 |
Visual object tracking is one of the most important task for security and surveillance applications. Recently, many came... [more] | PRMU2014-150 CNR2014-65 pp.185-190 |
|
| MI | 2014-01-26 15:45 |
Okinawa | Bunka Tenbusu Kan | A Preliminary Study on Organ Segmentation using Conditional Random Fields from Medical Image Yukitaka Nimura, Yuichiro Hayashi (Nagoya Univ.), Takayuki Kitasaka (Aichi Inst. of Tech.), Kensaku Mori (Nagoya Univ.) MI2013-84 |
This paper describes an organ region segmentation method using conditional random fields from medical images. A lot of m... [more] | MI2013-84 pp.155-160 |
| IBISML | 2013-11-12 15:45 |
Tokyo | Tokyo Institute of Technology, Kuramae-Kaikan | [Poster Presentation]
Performance Comparisons between Dependency Networks and Bayesian Networks Kazuya Takabatake, Shotaro Akaho (AIST) IBISML2013-41 |
Dependency networks are graphical models in which tasks of learning are done by totally local and simple algorithms of i... [more] | IBISML2013-41 pp.39-44 |
| SP, IPSJ-SLP (Joint) |
2013-07-26 10:30 |
Miyagi | Soho (togatta spa) | Grapheme-to-phoneme Conversion based on Adaptive Regularization of Weight Vectors Keigo Kubo, Sakriani Sakti, Graham Neubig, Tomoki Toda, Satoshi Nakamura (NAIST) SP2013-57 |
The current state-of-the-art approach in grapheme-to-phoneme (g2p) conversion is structured learning based on the Margin... [more] | SP2013-57 pp.25-30 |
| IBISML | 2012-11-07 15:30 |
Tokyo | Bunkyo School Building, Tokyo Campus, Tsukuba Univ. | Online Large-margin Weight Learning for First-order Logic-based Abduction Naoya Inoue, Kazeto Yamamoto, Yotaro Watanabe, Naoaki Okazaki, Kentaro Inui (Tohoku Univ.) IBISML2012-54 |
Abduction is inference to the best explanation. Abduction has long been studied in a wide range of contexts and is widel... [more] | IBISML2012-54 pp.143-150 |
| IBISML | 2010-11-04 15:00 |
Tokyo | IIS, Univ. of Tokyo | [Poster Presentation]
Multilayer Sequence Labeling Ai Azuma, Yuji Matsumoto (NAIST) IBISML2010-75 |
Sequence labeling has wide application areas such as natural language processing. In real world tasks, we often need to ... [more] | IBISML2010-75 pp.119-126 |
| NC, MBE (Joint) |
2010-03-09 14:35 |
Tokyo | Tamagawa University | Localization of Robots Based on Learning of Filters for Image features Mariko Oki, Masumi Ishikawa (Kyushu Inst. of Tech.) NC2009-107 |
In feature-based localization of a mobile robot, it is difficult to decide what features to use for localization.To trai... [more] | NC2009-107 pp.113-118 |
| NLP | 2009-12-21 13:50 |
Iwate | A Searching Method of Plural Dynamic Bayesian Networks Structures Using an Evolutionary Algorithm Kousuke Shibata, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) NLP2009-132 |
In this paper, the author presents an Immune Algorithm(IA) for learning the network structure of DBNs. In the convention... [more] | NLP2009-132 pp.33-36 |
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