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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IA | 2019-09-05 14:05 |
Hokkaido | Hokkaido Univ. Humanities and Social Sciences Classroom Bldg, W102 | A Study on Estimating Communication Delays using Graph Convolutional Networks with Semi-Supervised Learning Taisei Suzuki, Yuichi Yasuda, Ryo Nakamura, Hiroyuki Ohsaki (Kwansei Gakuin Univ.)  IA2019-10 |
In large-scale communication networks consisting of many end hosts and routers, accurate acquisition, measurement, and e... [more] | IA2019-10 pp.1-6 |
| SeMI, RCS, NS, SR, RCC (Joint) |
2019-07-11 15:00 |
Osaka | I-Site Nanba（Osaka） | Deep learning-based classification for the automatic of eNodeB state management in LTE networks Kazuki Hara (Tsukuba Univ.), Kohei Shiomoto (TCU), Chin Lam Eng, Sebastian Backstad (Ericsson Japan) RCC2019-41 NS2019-77 RCS2019-134 SR2019-53 SeMI2019-50 |
It is crucial to identify the cause immediately when a failure occurs at base station of mobile communication. However, ... [more] | RCC2019-41 NS2019-77 RCS2019-134 SR2019-53 SeMI2019-50 pp.145-150(RCC), pp.171-176(NS), pp.167-172(RCS), pp.177-182(SR), pp.159-164(SeMI) |
| PRMU, BioX | 2019-03-18 10:00 |
Tokyo | A Generative Self-Ensemble Approach to Simulated+Unsupervised Learning Yu Mitsuzumi (Kyoto Univ.), Go Irie (NTT), Atsushi Nakazawa (Kyoto Univ.), Akisato Kimura (NTT) BioX2018-52 PRMU2018-156 |
The simulated and unsupervised (S+U) learning framework is an effective approach in computer vision since it solves vari... [more] | BioX2018-52 PRMU2018-156 pp.137-142 |
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| IN, NS (Joint) |
2019-03-04 09:00 |
Okinawa | Okinawa Convention Center | Intrusion Detection System using semi-supervised learning with Adversarial Autoencoder Kazuki Hara, Kohei Shiomoto (Tokyo City Univ.) NS2018-193 |
In recent years the importance of intrusion detection system(IDS) is increasing. In particular, a method using machine l... [more] | NS2018-193 pp.1-6 |
| PRMU | 2018-12-13 14:55 |
Miyagi | Fast Distributional Smoothing for CTC-VAT and its Application to Text Line Recognition Ryohei Tanaka, Soichiro Ono, Akio Furuhata (Toshiba Digital Solutions) PRMU2018-80 |
Virtual Adversarial Training (VAT), which smooths posterior distribution by minimizing distributional distance of poster... [more] | PRMU2018-80 pp.29-34 |
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| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Asymptotic Evaluation of Classification in the Presence of Generalized Label Noise Goki Yasuda, Tota Suko, Manabu Kobayashi, Toshiyasu Matsushima (Waseda Univ.) IBISML2018-84 |
In classification problem, there are many cases where noise is added to the label. Learning in these cases is called lea... [more] | IBISML2018-84 pp.301-306 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Classification Algorithms for Generalized Label Noise Model with Unknown Parameter Tota Suko, Goki Yasuda, Shunsuke Horii, Manabu Kobayashi (Waseda Univ) IBISML2018-92 |
In classification problem, there is a case where noise is added to the label. The generalized label noise model is a mod... [more] | IBISML2018-92 pp.361-366 |
| NLC, IPSJ-DC | 2018-09-07 15:30 |
Tokyo | Seikei University | [Invited Lecture]
Toru Shimizu (Yahoo Japan) NLC2018-24 |
In this presentation, we report highlights from 56th Annual Meeting of the Association for Computational Linguistics (AC... [more] | NLC2018-24 p.93 |
| SP | 2018-08-27 14:20 |
Kyoto | Kyoto Univ. | [Invited Talk]
Product models and semi-supervised word segmentation Daichi Mochihashi (ISM) SP2018-28 |
While deep learning methods have achieved revolutionary success in speech and audio research, the impact is less signif... [more] |
SP2018-28 p.29 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning Tomoya Sakai, Gang Niu (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-40 |
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classif... [more] | IBISML2017-40 pp.39-46 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | The Classification Problem in Generalized Label Noise Model Tota Suko, Shunsuke Horii (Waseda Univ) IBISML2017-87 |
In classification problem, there is a case where noise is added to the label. In this study, we proposes a general nois... [more] |
IBISML2017-87 pp.377-382 |
| PRMU, BioX | 2017-03-20 10:00 |
Aichi | Selection of Near-Boundary Data for Semi-Supervised Learning Ryohei Tanaka, Xiao Ding, Soichiro Ono, Akio Furuhata (Toshiba) BioX2016-33 PRMU2016-196 |
Semi-supervised learning (SSL) is a technique which makes use of unlabeled data in addition to labeled data to obtain be... [more] | BioX2016-33 PRMU2016-196 pp.1-6 |
|
| PRMU, BioX | 2017-03-21 14:35 |
Aichi | Object grasping by multi-fingered robot based on transcription of human hand shape Hiroya Fukuhara, Kawakami Takuya, Yano Masaki, Matsuo Tadashi, Shimada Nobutaka (Ritsumeikan Univ) BioX2016-65 PRMU2016-228 |
In this research，we use the depth image of the human hand recalled as input and wake the robot hand of 3 fingers × 3 joi... [more] | BioX2016-65 PRMU2016-228 pp.191-196 |
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| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. | Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu (UTokyo), Masashi Sugiyama (RIKEN/UTokyo) IBISML2016-80 |
Most of the semi-supervised learning methods developed so far use unlabeled data for regularization purposes under parti... [more] | IBISML2016-80 pp.243-250 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2016-07-06 10:25 |
Okinawa | Okinawa Institute of Science and Technology | A Semi-supervised Learning Method for Imbalanced Binary Classification Akinori Fujino, Naonori Ueda (NTT) IBISML2016-3 |
This paper presents a semi-supervised learning method for imbalanced binary classification where the number of positive ... [more] | IBISML2016-3 pp.195-200 |
| EA, SP, SIP | 2016-03-29 13:15 |
Oita | Beppu International Convention Center B-ConPlaza | Effective basis learning for sound source separation by semi-supervised nonnegative matrix factorization Daichi Kitamura (SOKENDAI), Nobutaka Ono (NII/SOKENDAI), Hiroshi Saruwatari (UT), Yu Takahashi, Kazunobu Kondo (Yamaha) EA2015-130 SIP2015-179 SP2015-158 |
This paper addresses a sound source separation problem and proposes an effective basis learning method for semi-supervis... [more] | EA2015-130 SIP2015-179 SP2015-158 pp.355-360 |
| PRMU, IPSJ-CVIM, MVE [detail] | 2016-01-22 15:35 |
Osaka | A Note on the Computational Complexity Reduction Method of the Optimal Prediction under Bayes Criterion in Semi-Supervised Learning Yuto Nakano, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) PRMU2015-130 MVE2015-52 |
In this paper, we deal with a prediction problem of the semi-supervised learning based on the statistical decision theor... [more] | PRMU2015-130 MVE2015-52 pp.275-280 |
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| PRMU, BioX | 2015-03-19 16:40 |
Kanagawa | Semi-Supervised Low-rank GMM Learning for Multimodal Distribution Eita Aoki, Tetsuya Matsumoto, Noboru Ohnishi (Nagoya Univ.) BioX2014-60 PRMU2014-180 |
In this study, we examine the learning method of each category distribution under underdetermined environment that categ... [more] | BioX2014-60 PRMU2014-180 pp.129-134 |
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| NC, MBE | 2015-03-16 10:55 |
Tokyo | Tamagawa University | Feature Analysis for Diffuse Lung Disease with Deep Convolutional Neural Network Satoshi Suzuki, Hayaru Shouno (UEC), Shoji Kido (Yamaguchi Univ.) MBE2014-163 NC2014-114 |
The computer aided diagnosis (CAD) system is desired to develop for supporing physicians to diagnose the diffuse lung di... [more] | MBE2014-163 NC2014-114 pp.259-264 |
| NC, MBE (Joint) |
2014-03-17 15:00 |
Tokyo | Tamagawa University | Visualization using Supervised Generative Topographic Mapping Nobuhiko Yamaguchi (Saga Univ.) NC2013-97 |
Generative Topographic Mapping (GTM) is a nonlinear latent variable model introduced by Bishop et al. as a data visualiz... [more] | NC2013-97 pp.53-58 |
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