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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RCC, ISEC, IT, WBS | 2024-03-13 15:05 |
Osaka | Osaka Univ. (Suita Campus) | Efficient Replay Data Selection in Continual Federated Learning Model Yuto Kitano (Kobe Univ), Lihua Wang (NICT), Seiichi Ozawa (Kobe Univ)  IT2023-96 ISEC2023-95 WBS2023-84 RCC2023-78 |
In this study, we propose a continual federated learning that can continuously learn distributed data generated daily by... [more] | IT2023-96 ISEC2023-95 WBS2023-84 RCC2023-78 pp.135-141 |
| MI, MICT | 2023-11-14 13:40 |
Fukuoka | Arrhythmia Classification From Electrocardiogram by Gradient Boosting and Physician's Diagnosis-based algorithm. Haruto Shirae, Nobuhiro Nishii, Hiroshi Morita (Okayama Univ.), Ken'ichi Morooka (Kumamoto Univ.) MICT2023-31 MI2023-24 |
Implantable cardiac electrical devices can record a variety of arrhythmic events, but the determination of supraventricu... [more] | MICT2023-31 MI2023-24 pp.25-28 |
|
| LOIS | 2023-03-13 16:35 |
Okinawa | (Primary: On-site, Secondary: Online) |
Lifelog Data Analyses of SNS Users Based on Supervised Learning to Forecast the Number of Bookmarks of A Post Komei Arasawa, Shun Matsukawa, Nobuyuki Sugio, Naofumi Wada, Hiroki Matsuzaki (Hokkaido Univ. of Sci.) LOIS2022-56 |
It is an important issue to establish how to produce and transmit a post that triggers people's interest, in marketing a... [more] | LOIS2022-56 pp.72-76 |
| HWS, VLD | 2023-03-02 17:15 |
Okinawa | (Primary: On-site, Secondary: Online) |
Communication-Efficient Federated Learning with Gradient Boosting Decision Trees Kotaro Shimamura, Shinya Takamaeda (UTokyo) VLD2022-99 HWS2022-70 |
Federated learning (FL) is a machine learning method in which clients learn cooperatively without disclosing private dat... [more] | VLD2022-99 HWS2022-70 pp.137-142 |
| 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 |
| NLC | 2022-03-07 16:15 |
Online | Online | Program Information Extraction Using Gradient Boosting Hiroki Tanioka (Tokushima Univ.), Kenji Taniwaki (PLAT WORKS Corp.) NLC2021-37 |
Although video distribution services using the Internet have been launched one after another, the authors currently perf... [more] | NLC2021-37 pp.54-55 |
| SeMI | 2022-01-21 15:20 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
Asynchronous Gradient-Boosted Decision Trees for Distributed Sensing Devices Yui Yamashita, Akihito Taya, Yoshito Tobe (Aoyama Gakuin Univ.) SeMI2021-64 |
Recently, wearable devices that install multiple sensors have been widely used. Although sensor data from these devices ... [more] | SeMI2021-64 pp.45-47 |
| SANE | 2021-11-12 14:50 |
Online | Online | GPR data processing methods based on extrem gradient boosting algorithm to detect the backfill grouting of shield tunnel Xiongyao Xie, Li Zeng, Biao Zhou (Tongji Univ.) SANE2021-59 |
Shield tunnel method is currently the most important method for tunnel excavation in soft soil areas. With the construct... [more] | SANE2021-59 pp.144-148 |
| ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2020-02-28 14:25 |
Hokkaido | Hokkaido Univ. (Cancelled but technical report was issued) |
[Special Talk]
Infrastructure maintenance deta analysis
-- The survey of soundness judgement of bridges by machine learning -- Aoi Hasegawa, Yuki wakuda, Maiku Abe (Hokkaido Univ.), Hiromu Suzuki (NEXCO EAST) |
In this study, we investigate the use of machine learning to estimate the soundness of steel bridge RC slabs. Inspection... [more] | |
| IBISML | 2019-03-06 13:30 |
Tokyo | RIKEN AIP | Acceleration of Boosting Discriminators Using Region Partition Learning and Its Application to Face Detectors Takeshi Mori, Junichi Takeuchi, Masanori Kawakita (Kyushu Univ.) IBISML2018-115 |
We propose a method of acceleration of boosting discriminators. Discriminant functions used for boosting are constructe... [more] |
IBISML2018-115 pp.73-80 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Revising the Algorithm of Ensenble Learning by an Index of Complementarity among Weak Learners Shota Utsumi, Keisuke Kameyama (Univ. of Tsukuba) IBISML2018-102 |
In ensemble learning, the performance of each weak learner and their acquisition of complementary functions affects the ... [more] | IBISML2018-102 pp.429-434 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2018-09-20 09:40 |
Fukuoka | Arrangement of Complementary Weak Learners using Weights Assigned to Data in Parallel Ensemble Learning Shota Utsumi, Keisuke Kameyama (Univ. of Tsukuba) PRMU2018-37 IBISML2018-14 |
The accuracy of each weak learner and acquisition of complementary functions among weak learners are important for impro... [more] | PRMU2018-37 IBISML2018-14 pp.9-15 |
|
| ICD, CPSY, CAS | 2017-12-14 15:10 |
Okinawa | Art Hotel Ishigakijima | A 190mV Start-up Voltage Doubler Charge Pump with CMOS Gate Boosting Scheme using 0.18um Standard CMOS Process for Energy Harvesting Minori Yoshida, Kousuke Miyaji (Shinshu Univ.) CAS2017-91 ICD2017-79 CPSY2017-88 |
Recently, energy harvesting power supply circuit using a cold-start function for IoT devices is required to restore powe... [more] | CAS2017-91 ICD2017-79 CPSY2017-88 p.127 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | IBISML2017-85 |
We consider binary classification problems using local features of objects. One of motivating applications is time-serie... [more] | IBISML2017-85 pp.361-368 |
| MW (2nd) |
2017-06-14 - 2017-06-16 |
Overseas | KMUTT, Bangkok, Thailand | Gain-Boosted Feedback Amplifier Design Using Leaky Tapped Transformer Shingo Yasuda, Shuhei Amakawa (Hiroshima Univ.) |
This paper develops a theory on high-gain near-fmax feedback amplifier design using a tapped transformer with imperfect ... [more] | |
| SP, IPSJ-SLP (Joint) |
2014-07-25 09:30 |
Iwate | Hotel Hanamaki | A generalized discriminative training framework for system combination Yuuki Tachioka, Shinji Watanabe, Jonathan Le Roux, John Hershey (Mitsubishi Electric) SP2014-65 |
This paper proposes a generalized discriminative training framework for system combination, which encompasses acoustic m... [more] | SP2014-65 pp.13-18 |
| SP, IPSJ-MUS | 2014-05-24 11:30 |
Tokyo | Discriminative training of acoustic models for system combination Yuuki Tachioka (Mitsubishi Electric), Shinji Watanabe, Jonathan Le Roux, John R. Hershey (MERL) SP2014-15 |
In discriminative training methods, the objective function is designed to improve the performance of automatic speech re... [more] | SP2014-15 pp.147-152 |
|
| IBISML | 2013-11-12 15:45 |
Tokyo | Tokyo Institute of Technology, Kuramae-Kaikan | [Poster Presentation]
A boosting method considering tolerance against noisy data by weighting each data according to the distance between incidents Shinjiro Fujita, Sayaka Kamei, Satoshi Fujita (Hiroshima Univ.) IBISML2013-38 |
AdaBoost is one of the major ensemble learning methods. It is easy to implement and has high classification accuracy. ... [more] |
IBISML2013-38 pp.15-21 |
| PRMU, MVE, IPSJ-CVIM (Joint) [detail] |
2013-01-24 15:45 |
Kyoto | Hybrid Transfer Learning for Efficient Learning in Object Detection Masamitsu Tsuchiya, Yuji Yamauchi (Chubu Univ.), Takayoshi Yamashita (Omron Corp.), Hironobu Fujiyoshi (Chubu Univ.) PRMU2012-122 MVE2012-87 |
In the detection of human from image using statistical learning methods, the labor cost of collecting training samples a... [more] | PRMU2012-122 MVE2012-87 pp.329-334 |
|
| IBISML | 2012-06-20 14:20 |
Kyoto | Campus plaza Kyoto | Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering Ildefons Magrans de Abril, Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2012-12 |
This paper presents the ideas and methods of the winning solution for the Kaggle Algorithmic Trading Challenge. This an... [more] | IBISML2012-12 pp.79-84 |
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