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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 31  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
RCC, ISEC, IT, WBS 2024-03-13
- 2024-03-14
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
EMM 2024-01-16
15:25
Miyagi Tohoku Univ.
(Primary: On-site, Secondary: Online)
[Invited Talk] Federated Learning with Enhanced Privacy Protection in AI
Lihua Wang (NICT) EMM2023-83
Federated learning is a crucial methodology in artificial intelligence where multiple organizations collaborate to perfo... [more] EMM2023-83
p.19
ICM, NS, CQ, NV
(Joint)
2023-11-22
09:25
Ehime Ehime Prefecture Gender Equality Center
(Primary: On-site, Secondary: Online)
A Study on Transfer of Decision Tree for Operation of Future Managed Networks
Takaaki Moriya, Takashi Mukai, Manabu Nishio, Ai Tsunoda, Ken Kanishima (NTT) ICM2023-26
When we build a new managed network, we need knowledge to solve various failures that will be occurred in the network. H... [more] ICM2023-26
pp.20-25
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
KBSE, SWIM 2022-05-20
14:00
Tokyo
(Primary: On-site, Secondary: Online)
Generating LTL formulas with Trace Examples
Kota Komatsu, Hiroki Horita (Ibaraki Univ.) KBSE2022-1 SWIM2022-1
In declarative process mining, a user can verify a desired property in a business process by providing an LTL formula wi... [more] KBSE2022-1 SWIM2022-1
pp.1-6
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
SIP, IT, RCS 2021-01-22
09:50
Online Online Selection of Interference Cancellation Technique Using Decision Trees for Uplink Non-orthogonal Multiple Access -- Evaluation of Communication Success Rate in A Mobile Environment --
Noriaki Yamamoto (Meiji Univ.), Masafumi Moriyama, Kenichi Takizawa (NICT), Tetsushi Ikegami (Meiji Univ.) IT2020-85 SIP2020-63 RCS2020-176
As IoT(Internet of Things)develops, wireless access techniques that can effectively accommodate massive number of device... [more] IT2020-85 SIP2020-63 RCS2020-176
pp.119-124
HWS, VLD [detail] 2020-03-06
10:30
Okinawa Okinawa Ken Seinen Kaikan
(Cancelled but technical report was issued)
A Consideration on Efficient Anomaly Detection Based on Isolation Forest
Tsubasa Ikeda, Shinobu Nagayama, Masato Inagi, Shin'ichi Wakabayashi (HCU) VLD2019-125 HWS2019-98
Isolation Forest is a method to detect anomalies by ensemble learning of binary decision trees. It traverses each decisi... [more] VLD2019-125 HWS2019-98
pp.179-184
NS, ICM, CQ, NV
(Joint)
2019-11-21
11:20
Hyogo Rokkodai 2nd Campus, Kobe Univ. Experiment of DDoS Attack Detection with UTM Log Analysis Using Decision Tree
Aoshi Fujioka, Hiroyuki Okazaki, Hikofumi Suzuki (Shinshu Univ.) NS2019-123
In recent years, AI technology such as machine learning has been developed and the number of application examples are in... [more] NS2019-123
pp.19-25
IT 2019-07-25
14:50
Tokyo NATULUCK-Iidabashi-Higashiguchi Ekimaeten Bayes Optimal Classification on Decision Tree Model and Its Approximative Algorithm Using Ensemble Learning
Nao Dobashi, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2019-17
In this paper we consider classification problem about discrete category $y$ regarding discrete variables $bm{x}$. Deci... [more] IT2019-17
pp.11-16
RCC, MICT 2019-05-29
15:30
Tokyo TOKYO BIG SIGHT [Poster Presentation] Development of Real-time Body Motion Identification System using Radio Channel Characteristics in Wireless BAN
Yuki Ichikawa, Masahiro Mitta, Minseok Kim (Niigata Univ.) RCC2019-7 MICT2019-7
In this article, a real-time motion identification system is presented which has been developed based on the knowledge o... [more] RCC2019-7 MICT2019-7
pp.29-33
RCC, MICT 2018-05-24
13:00
Tokyo Tokyo Big Sight [Poster Presentation] Investigation of Feature Reduction for Body Motion Identification using Radio Channel Characteristics in Wireless BAN
Yuki Ichikawa, Minseok Kim (Niigata Univ.) RCC2018-4 MICT2018-4
In this article, the reduction of the features used for human motion classification using decision tree machine learning... [more] RCC2018-4 MICT2018-4
pp.17-20
IBISML 2018-03-05
13:50
Fukuoka Nishijin Plaza, Kyushu University Metric Learning for k-Nearest Neighbor Estimation using Multiple Distance Metrics
Yokuto Seki, Noboru Murata (Waseda Univ.) IBISML2017-92
The relationship between unstructured datasets such as graphs can be measured by multiple distance metrics.
In this pap... [more]
IBISML2017-92
pp.15-19
IBISML 2017-11-09
13:00
Tokyo Univ. of Tokyo [Poster Presentation] Empirical Bayesian Tree
Masashi Sekino (SMN) IBISML2017-39
We propose a new decision tree learning algorithm ``Empirical Bayesian Tree (EBT)'', which models the outputs of a leaf ... [more] IBISML2017-39
pp.31-38
KBSE 2017-09-19
12:35
Tokyo Doshisha Univ. Tokyo Branch Office A load balancing mechanism using shared state for tree processing in actor model
Kouhei Sakurai (Kanazawa Univ.) KBSE2017-21
For machine learning and data mining, there are methods that deal with tree structure, and also their parallelization an... [more] KBSE2017-21
pp.1-6
PRMU, IBISML, IPSJ-CVIM [detail] 2017-09-15
13:30
Tokyo   Fast and General-Purpose Bayesian Optimization using Tree-Based Model with Gaussian Process
Hiroo Iwanaga (Univ. of Tokyo/NTT DATA MSI), Yukio Ohsawa (Univ. of Tokyo) PRMU2017-48 IBISML2017-20
Bayesian optimization is an effective method for black-box optimization problems such as hyperparameter tuning of machin... [more] PRMU2017-48 IBISML2017-20
pp.67-74
ASN, MoNA, MICT
(Joint)
2017-01-20
14:15
Oita   An Investigation of Body Motion Identification Method using Radio Channel Characteristics for BAN Context-Aware Communications
Yuki Ichikawa, Minseok Kim (Niigata Univ.) MICT2016-74
In this article, human motion classification to realize context-aware BAN has been empirically investigated. We develope... [more] MICT2016-74
pp.53-56
AI 2015-12-18
14:30
Okinawa   Comparison of SVM and Decision Tree for Prediction of Postoperative Hospital Stay
Yuusuke Adachi, Takanori Yamashita, Yosifumi Wakata (Kyushu Univ.), Hidehisa Soejima (Saiseikai Kumamoto Hospital), Naoki Nakashima, Sachio Hirokawa (Kyushu Univ.) AI2015-36
In the medical practice, vast medical data are accumulated every day with medical computerization now. Due to increasin... [more] AI2015-36
pp.59-64
 Results 1 - 20 of 31  /  [Next]  
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