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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MSS, CAS, SIP, VLD | 2023-07-06 10:40 |
Hokkaido | (Primary: On-site, Secondary: Online) |
Autoencoder Based Incremental LSI Test Escape Detection Using Transfer Learning Ayano Takaya, Michihiro Shintani (KIT)  CAS2023-4 VLD2023-4 SIP2023-20 MSS2023-4 |
Machine-learning-based test escape detection is gaining attention as a novel approach for detecting faulty large-scale i... [more] | CAS2023-4 VLD2023-4 SIP2023-20 MSS2023-4 pp.16-21 |
| NC, MBE (Joint) |
2023-03-15 10:55 |
Tokyo | The Univ. of Electro-Communications (Primary: On-site, Secondary: Online) |
Proposal for Mini-Batch Learning in Clustering V-SOINN Tetsuya Komura, Rintaro Funada, Yukari Yamauchi (Nihon Univ.) NC2022-111 |
Yamazaki et al. proposed a learning method called Self-Organizing Incremental Neural Network (SOINN). This method is an ... [more] | NC2022-111 pp.109-112 |
| NC, MBE (Joint) |
2023-03-15 11:20 |
Tokyo | The Univ. of Electro-Communications (Primary: On-site, Secondary: Online) |
Optimizing SOINN Space for High-Dimensional Data Robustness Yu Takahagi, Yusuke Tsuchida, Yukari Yamauchi (Nihon Univ.) NC2022-112 |
Yamazaki et al. proposed a learning method called Self-Organizing Incremental Neural Network (SOINN). This method is an ... [more] | NC2022-112 pp.113-118 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 13:00 |
Online | Online | Domain Incremental Leaning with Adaptive Loss Functions Takumi Kawashima (UTokyo), Go Irie, Daiki Ikami (NTT), Kiyoharu Aizawa (UTokyo) ITS2021-30 IE2021-39 |
During domain incremental learning of image classification task, the distribution of images continually change, and mode... [more] | ITS2021-30 IE2021-39 pp.31-36 |
| NC, MBE (Joint) |
2021-03-04 16:25 |
Online | Online | Hierarchical Feature Extraction for Dynamic Q-Network Taishi Komatsu, Yukari Yamauchi (Nihon Univ.) NC2020-62 |
Recently, Convolutional Neural Networks (CNN), which have been successful in the field of image recognition, use a hiera... [more] | NC2020-62 pp.112-116 |
| PRMU | 2020-12-17 16:20 |
Online | Online | [Short Paper]
Few-Shot Incremental Learning by Unifying with Variational Autoencoder Keita Takayama, Kuniaki Uto, Koichi Shinoda (TokyoTech) PRMU2020-48 |
We propose a few-shot incremental learning method using a variational autoencoder for deep learning. In incremental lear... [more] | PRMU2020-48 pp.58-62 |
| NC, MBE (Joint) |
2020-03-06 10:20 |
Tokyo | University of Electro Communications (Cancelled but technical report was issued) |
Feature Extraction by Competitive Learning for Dynamic Q-Network Taishi Komatsu, Yukari Yamauchi (NU) NC2019-106 |
Deep Q-Network is a reinforcement learning algorithm that performs feature extraction by convolution from state space in... [more] | NC2019-106 pp.175-179 |
| 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, CNR | 2017-02-19 10:45 |
Hokkaido | Incremental Personalization of Image Classifiers Shota Horiguchi, Sosuke Amano, Kiyoharu Aizawa (UTokyo), Makoto Ogawa (foo.log) PRMU2016-178 CNR2016-45 |
(To be available after the conference date) [more] | PRMU2016-178 CNR2016-45 pp.149-154 |
|
| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. | Incremental Natural Actor Critic with Importance Weight Aware Update Ryo Iwaki (Osaka Univ.), Hiroki Yokoyama (Tamagawa Univ.), Minoru Asada (Osaka Univ.) IBISML2016-81 |
Appropriate tuning of step-size parameter is crucial for reinforcement learning, as well as other machine learning techn... [more] | IBISML2016-81 pp.251-257 |
| PRMU, SP, WIT, ASJ-H | 2016-06-13 14:30 |
Tokyo | A Method for Adding Classification Classes in Random Forests Ryota Ozaki, Yukihiro Tsuboshita, Noriji Kato (Fuji Xerox) PRMU2016-42 SP2016-8 WIT2016-8 |
In estimating classes using Random Forests, it is necessary to re-create decision trees with the data of all classes whe... [more] | PRMU2016-42 SP2016-8 WIT2016-8 pp.41-45 |
|
| ICSS, IPSJ-SPT | 2016-03-04 14:30 |
Kyoto | Academic Center for Computing and Media Studies, Kyoto University | An Autonomous DDoS Backscatter Detection System from Darknet Traffic Yuki Ukawa, Jun Kitazono, Seiichi Ozawa (Kobe Univ.), Tao Ban, Junji Nakazato (NICT), Jumpei Shimamura (clwit) ICSS2015-67 |
This paper proposes an autonomous DDoS backscatter detection system from UDP darknet traffic. To identify DDoS backscatt... [more] | ICSS2015-67 pp.123-128 |
| MBE, NC (Joint) |
2015-12-19 13:00 |
Aichi | Nagoya Institute of Technology | Limited General Regression Neural Network for embedded systems and its implementation method to increase its throughput Daisuke Nishio, Koichiro Yamauchi (Chubu Univ.) NC2015-46 |
Recent improvement of the microcomputer enables the execution of complex intelligent algorithms on embedded systems. ... [more] |
NC2015-46 pp.1-6 |
| NC, MBE | 2015-03-16 15:10 |
Tokyo | Tamagawa University | A Proposal of Novel Data Detection Method and Its Application to Incremental Learning for RBMs Masahiko Osawa, Masafumi Hagiwara (Keio Univ.) MBE2014-167 NC2014-118 |
Incremental learnings without destruction of the existing memory are often difficult for deep learning, since most of th... [more] | MBE2014-167 NC2014-118 pp.283-288 |
| MBE, NC (Joint) |
2014-11-21 16:15 |
Miyagi | Tohoku University | A Study on utility in clinical practice with P300 speller Kohei Kawai, Tomohiro Yoshikawa, Takeshi Furuhashi (Nagoya Univ.) MBE2014-64 NC2014-34 |
In this paper, we discuss about the performance of spelling with P300 speller in clinical practice. P300 speller is one... [more] |
MBE2014-64 NC2014-34 pp.31-34(MBE), pp.47-50(NC) |
| PRMU, CNR | 2014-02-13 16:10 |
Fukuoka | [Poster Presentation]
Incremental Learning of Hand Gestures based on Subunit Sequences Ryo Kawahata, Yanrung Wan, Atsushi Shimada (Kyushu Univ.), Takayoshi Yamashita (OMRON), Rin-ichiro Taniguchi (Kyushu Univ.) PRMU2013-141 CNR2013-49 |
We propose an incremental learning of hand gestures based on subunit sequences. A subunit based DTW method can shorten r... [more] | PRMU2013-141 CNR2013-49 pp.97-98 |
|
| IBISML | 2013-11-13 15:45 |
Tokyo | Tokyo Institute of Technology, Kuramae-Kaikan | [Poster Presentation]
Safe Screening Rule for Incmrenetal Learning Yoshiki Suzuki, Shota Okumura, Kohei Ogawa, Ichiro Takeuchi (Nagoya Inst. of Tech.) IBISML2013-64 |
Efficient optimization algorithm is required in online learning or other incremental learning scenario since the model m... [more] | IBISML2013-64 pp.213-218 |
| MBE, NC (Joint) |
2013-03-13 13:20 |
Tokyo | Tamagawa University | A Proposal of Incremental Learning on Spiking Hopfield Network Hideaki Ikeda, Yukari Yamauchi (Nihon Univ.) NC2012-142 |
This research proposes Spiking Hopfield Network with input layer expanded from a research of SASAKI et al. It has a mech... [more] | NC2012-142 pp.49-54 |
| PRMU | 2013-02-21 15:00 |
Osaka | Fast online multivariate non-parametric density estimation under noisy environment Yoshihiro Nakamura, Hiroshi Takahashi, Osamu Hasegawa (Titech) PRMU2012-141 |
Recently, with the development of network and sensors technology, a huge number of data are generating continuously in r... [more] | PRMU2012-141 pp.67-72 |
|
| MBE, NC (Joint) |
2012-03-14 16:00 |
Tokyo | Tamagawa University | Incremental Learning for regression on a budget Koichiro Yamauchi (Chubu Univ) NC2011-145 |
In our previous work, we had proposed a limited general regression neural network (LGRNN) for embedded systems. The LGR... [more] |
NC2011-145 pp.141-146 |
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