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All Technical Committee Conferences  (Searched in: Recent 10 Years)

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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 17 of 17  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
AI 2025-02-19
12:10
Aichi Nagoya Institute of Technology (Aichi) Extraction of Hierarchical Structures Formed by High-Dimensional Swarming Motion via Topological Data Analysis
Shinichi Ishida (Hitotsubashi Univ.), Norihiro Maruyama, Takashi Ikegami (UT), Yoh-ichi Mototake (Hitotsubashi Univ.) AI2024-17
In recent years, research on flock dynamics in animals and cells has garnered increasing attention in both scientific an... [more] AI2024-17
pp.49-53
R 2023-03-10
13:50
Hiroshima (Hiroshima, Online)
(Primary: On-site, Secondary: Online)
Failure Sign Detection by State Path Analysis for Fare Collection System -- Evaluation by Sequential Pattern Mining with Mechatronics Knowledge --
Ken Ueno, Misato Ishikawa, Yuko Kobayashi, Takamitsu Sunaoshi (Toshiba), Kiyoku Endo (Toshiba Automation Systems Service) R2022-50
To detect failure sign on Fare Collection System (FCS) which has low failure rate accurately, we need the mechatronics k... [more] R2022-50
pp.13-18
IBISML 2022-09-15
15:05
Kanagawa Keio Univ. (Yagami Campus) (Kanagawa, Online)
(Primary: On-site, Secondary: Online)
Improving Efficiency of Regularization Path Computation in Safe Pattern Pruning via Multiple Referential Solutions
Takumi Yoshida (Nitech), Hiroyuki Hanada (RIKEN), Kazuya Nakagawa, Shinya Suzumura, Onur Boyar, Kazuki Iwata (Nitech), Shun Shimura, Yuji Tanaka (NaogyaU), Masayuki Karasuyama (Nitech), Kouichi Taji (NaogyaU), Koji Tsuda (UTokyo/RIKEN), Ichiro Takeuchi (NaogyaU/RIKEN) IBISML2022-38
Safe Screening and Safe Pattern Pruning are methods for efficiently modeling high-dimensional features by $L_1$-regulari... [more] IBISML2022-38
pp.39-46
IBISML 2020-10-22
10:25
Online Online (Online) IBISML2020-28 I will introduce significant pattern mining techniques that are designed to find statistically significant interactions ... [more] IBISML2020-28
p.47
SS, MSS 2020-01-14
16:30
Hiroshima (Hiroshima) A Prototype Algorithm and Implementation of Code-Clone Detection with MapReduce-Style Prallel Processing
Toshihiro Kamiya (Shimane Univ.), Eunjong Choi (KIT) MSS2019-48 SS2019-32
This report presents a scalable type-3 code-clone (duplicated code) detection method running in a
MapReduce style paral... [more]
MSS2019-48 SS2019-32
pp.43-48
RISING
(2nd)
2019-11-26
14:10
Tokyo Fukutake Learning Theater, Hongo Campus, Univ. Tokyo (Tokyo) Due to the sophistication and diversification of information infrastructure in recent years, the risk due to connections... [more]
IBISML 2018-11-05
15:10
Hokkaido Hokkaido Citizens Activites Center (Kaderu 2.7) (Hokkaido) [Poster Presentation] Statistical test for extracting discriminative patterns from trajectory data
Takuto Sakuma (NITech), Ichiro Takeuchi (NITech/NIMS/RIKEN) IBISML2018-72
In this paper, we propose a statistical test method for extracting characteristic subsequences (patterns) from trajector... [more] IBISML2018-72
pp.213-220
DE, IPSJ-DBS, IPSJ-IFAT 2018-09-12
14:50
Tokyo (Tokyo, Overseas) Modeling Item Selection Process on Online Shopping.
Fumiya Watanabe, Yuri Nozaki, Tetsuji Satoh (Univ. of Tsukuba) DE2018-16
In recent years, opportunities to purchase products on online shopping sites are increasing.
One of the features of onl... [more]
DE2018-16
pp.43-47
IBISML 2018-03-06
10:00
Fukuoka Nishijin Plaza, Kyushu University (Fukuoka) Learning rule-base model by Safe Pattern Pruning
Hiroki Kato, Hiroyuki Hanada (Nagoya Inst. of Tech.), Ichiro Takeuchi (Nagoya Inst. of Tech./RIKEN/NIMS) IBISML2017-98
We consider learning the prediction model called ''rule-base model''. Rule-base model is the model which uses ''rules'' ... [more] IBISML2017-98
pp.55-62
KBSE 2018-03-02
11:10
Okinawa (Okinawa) Identification of Aggregation Roots and Providing Monotonous Views by Applying Existence Dependency Analysis to the Business Fulfilments
Akio Ida, Shigeo Kaneda, Yusuke Morimoto, Liu Xiangtao (Doshisha Unic.) KBSE2017-56
Many of the conventional methods combine the attributes of entities in an ad hoc manner to acquire View. In some cases, ... [more] KBSE2017-56
pp.103-108
IBISML 2017-03-07
10:00
Tokyo Tokyo Institute of Technology (Tokyo) Safe Pruning Rule for Predictive Sequential Pattern Mining and Its Application to Bio-logging Data Analysis
Kaoru Kishimoto (NITech), Masayuki Karasuyama (NIT), Kazuya Nakagawa (NITech), Kotaro Kimura (Osaka Univ.), Ken Yoda (Nagoya Univ.), Yuta Umezu, Shinsuke Kajioka (NITech), Koji Tsuda (UTokyo), Ichiro Takeuchi (NITech) IBISML2016-105
Recently, the analysis for time-series logging data of animal behaviors, called bio-logging data, has attracted a wide a... [more] IBISML2016-105
pp.41-48
ET 2017-01-28
16:30
Kanagawa National Institute of Special Needs Education (Kanagawa) Development of a Method for Analyzing Source Code Editing Processes to Estimate Students' Learning Situations
Kei Ishiwada, Yasuhiko Morimoto (TGU), Shoichi Nakamura, Hiroki Nakayama (FU), Youzou Miyadera (TGU) ET2016-92
In the programming exercise lesson, it is expected that estimating learning situation based on the source code editing p... [more] ET2016-92
pp.75-80
PRMU, IPSJ-CVIM, IBISML [detail] 2016-09-05
15:45
Toyama (Toyama) Sparse learning for pattern mining problem by using Safe Pattern Pruning method
Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama (NIT), Koji Tsuda (Univ. of Tokyo), Ichiro Takeuchi (NIT) PRMU2016-70 IBISML2016-25
In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a s... [more] PRMU2016-70 IBISML2016-25
pp.127-134
ET 2016-07-09
11:50
Miyagi Tohoku Gakuin University (Miyagi) Development of a Tool for Analyzing the Edit Histories of Programs to Estimate the Learning Situations
Kei Ishiwada, Yasuhiko Morimoto (Tokyo Gakugei Univ.), Shoichi Nakamura (Fukushima Univ.), Youzou Miyadera (Tokyo Gakugei Univ.) ET2016-25
In this study, we aim to estimate students’ learning situations in the Programming Exercise class, based on their progra... [more] ET2016-25
pp.21-26
SS 2016-03-11
11:15
Okinawa (Okinawa) Preliminary Study of Selective Inlining to Find API Usage Patterns Across Multiple Methods
Yuya Aizawsa, Takashi Kobayashi (Tokyo Tech.) SS2015-96
The API usage pattern is useful for programmers to learn the usage of the library. In this report, we propose a method t... [more] SS2015-96
pp.121-126
SS 2015-05-12
08:45
Kumamoto Kumamoto University (Kumamoto) Coding Pattern Detection for C Programs Using Pattern Mining Technique
Yuta Nakamura, Eunjong Choi (Osaka Univ.), Norihiro Yoshida (Nagoya Univ.), Shusuke Haruna, Katsuro Inoue (Osaka Univ.) SS2015-9
Coding pattern is an idiomatic code fragment that distributes in modules. It can be used for finding bugs because it imp... [more] SS2015-9
pp.41-46
MSS 2015-03-05
15:30
Ishikawa IT Business Plaza Musashi (Ishikawa) Code clone detection using parallel distributed processing for software revision history
Shin Chadani, Kohei Sakurai, Satoshi Yamane (Kanazawa Univ.) MSS2014-94
In software development with a version control system, code clones detected from the version history of the code can be ... [more] MSS2014-94
pp.19-24
 Results 1 - 17 of 17  /   
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