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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-21 11:45 |
Hokkaido | Hokkaido Univ. | A Note on Improvement of Supervised Latent Variable Model with Graph-Encoded Class Information Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Supervised latent variable models aim to estimate a manifold from original data and supervised information, such as clas... [more] | |
| QIT (2nd) |
2022-12-08 18:15 |
Kanagawa | Keio Univ. (Primary: On-site, Secondary: Online) |
Quantum Fisher kernel for mitigating the vanishing similarity issue Yudai Suzuki, Hideaki Kawaguchi, Naoki Yamamoto (Keio Univ.) |
Quantum kernel method is a machine learning model exploiting quantum computers to calculate the quantum kernels (QKs) th... [more] | |
| MI | 2021-03-16 14:00 |
Online | Online | Deformable mesh registration of partial lung shapes based on learning of pneumothorax deformation Hinako Maekawa, Megumi Nakao (Kyoto Univ.), Katsutaka Mineura (Kyoto Univ. Hospital), Toyofumi F. Chen-Yoshikawa (Nagoya Univ. Hospital), Tetsuya Matsuda (Kyoto Univ.)  MI2020-74 |
Intraoperative pneumothorax is accompanied by large deformation including rotation. As intraoperative cone-beam CT (CBCT... [more] | MI2020-74 pp.112-117 |
| IBISML | 2020-01-09 15:45 |
Tokyo | ISM | IBISML2019-24 |
(To be available after the conference date) [more] | IBISML2019-24 pp.45-52 |
| R | 2019-06-14 15:30 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Identification comparison of software fault-prone modules using nonlinear logistic regression models Kazunari Yamanaka, Tadashi Dohi, Hiroyuki Okamura (Hiroshima U.) R2019-12 |
In this article, we compare several non-linear logistic regression models used in a fault-prone identification problem... [more] |
R2019-12 pp.19-24 |
| EMM, IE, LOIS, IEE-CMN, ITE-ME [detail] | 2017-09-04 13:50 |
Kyoto | Kyoto Univ. (Clock Tower Centennial Hall) | Privacy-preserving SVM processing by using random unitary transformation Takahiro Maekawa, Yuma Kinoshita, Sayaka Shiota, Hitoshi Kiya (Tokyo Metro.Univ.) LOIS2017-15 IE2017-36 EMM2017-44 |
In this paper,we propose a privacy-preserving SVM processing method with templates protected by using a random unitary t... [more] | LOIS2017-15 IE2017-36 EMM2017-44 pp.13-18 |
| ITS, IE, ITE-AIT, ITE-HI, ITE-ME, ITE-MMS, ITE-CE [detail] | 2016-02-22 10:00 |
Hokkaido | Hokkaido Univ. | Secure classification based on kernel method using unitary transformation Ibuki Nakamura, Yuko Saito, Sayaka Shiota, Hitoshi Kiya (Tokyo Metropolitan Univ.) ITS2015-59 IE2015-101 |
This study considers a template protection scheme based on an random unitary transformation, where the template consists... [more] | ITS2015-59 IE2015-101 pp.17-22 |
| PRMU | 2015-12-22 15:20 |
Nagano | A Survey of Modified Quadratic Discriminant Function and its Application Tomoki Terada, Wataru Ohyama, Tetsushi Wakabayashi, Fumitaka Kimura (Mie Univ) PRMU2015-113 |
Modified Quadratic Discriminant Function (MQDF) is a discriminant function which significantly contributed for performan... [more] | PRMU2015-113 pp.129-141 |
|
| SP, IPSJ-SLP (Joint) |
2014-07-25 14:20 |
Iwate | Hotel Hanamaki | [Invited Talk]
Karnel method for Bayesian inference and its applications Kenji Fukumizu (ISM) SP2014-69 |
As a kernel framework for statsitical inference, "kernel mean embedding" has been recently developed, in which probabili... [more] | SP2014-69 pp.37-40 |
| PRMU, IPSJ-CVIM, MVE [detail] | 2014-01-23 10:00 |
Osaka | Minimum Classification Error Training with Automatic Control of Loss Smoothness Hideaki Tanaka (Doshisha Univ.), Hideyuki Watanabe (NICT), Shigeru Katagiri, Miho Ohsaki (Doshisha Univ.), Shigeki Matsuda, Chiori Hori (NICT) PRMU2013-92 MVE2013-33 |
The Minimum Classification Error (MCE) training has been successfully applied to various types of classifiers. However, ... [more] | PRMU2013-92 MVE2013-33 pp.7-12 |
|
| NLP | 2012-05-29 11:20 |
Akita | Akita City Exchange Plaza | Iterative Discriminant Analysis in Non-linear Space Yohei Takeuchi, Momoyo Ito, Minoru Fukumi (Tokushima Univ.) NLP2012-37 |
In pattern recognition, Fisher Linear Discriminant Analysis (FLDA) is one of the most effective feature extraction metho... [more] | NLP2012-37 pp.59-64 |
| CAS, CS, SIP | 2012-03-09 15:40 |
Niigata | The University of Niigata | Short Term PV Prediction Using Committee Kernel Adaptive Filters Yuichiro Yoneda, Toshihisa Tanaka (TUAT) CAS2011-162 SIP2011-182 CS2011-154 |
We study short term power prediction of photovoltaic (PV) using data acquired from the PV panels. PV power outputs are u... [more] | CAS2011-162 SIP2011-182 CS2011-154 pp.309-314 |
| IBISML | 2011-11-10 15:45 |
Nara | Nara Womens Univ. | A Linear Time Subpath Kernel for Trees Daisuke Kimura, Hisashi Kashima (Univ. of Tokyo) IBISML2011-85 |
Kernel method is one of the promising approaches to learning with tree-structured data, and various efficient tree ker... [more] |
IBISML2011-85 pp.291-296 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2011-09-06 14:50 |
Hokkaido | A Method for Multiple Instance Learning Using Sparse Kernel Machines Kazuhisa Nagashima, Masato Inoue (Waseda Univ.) PRMU2011-77 IBISML2011-36 |
Multiple Instance Learning problem (MIL) is roughly one of the classification problems. In generally classification pr... [more] |
PRMU2011-77 IBISML2011-36 pp.159-163 |
|
| NC | 2011-07-25 15:40 |
Hyogo | Graduate School of Engineering, Kobe University | An Incremental Learning Algorithm of Kernel Principal Component Analysis for Chunk Data Takaomi Tokumoto, Seiichi Ozawa (Kobe Univ) NC2011-29 |
In this paper, a new algorithm for Kernel Principal Component Analysis (KPCA) is proposed. We extended Takeuchi et al's... [more] |
NC2011-29 pp.49-54 |
| NLP | 2011-03-10 14:20 |
Tokyo | Tokyo University of Science | A Manifold Learning Approach for Analyzing Chaos in A Dripping Faucet System Hiromichi Suetani (Kagoshima Univ./JST/RIKEN), Hiroki Kuroiwa, Hiroki Hata (Kagoshima Univ.), Shotaro Akaho (AIST) NLP2010-173 |
Dripping water from a faucet is very familiar to us and it provides various nonlinear phenomena including chaos. When i... [more] | NLP2010-173 pp.57-62 |
| IBISML | 2010-11-05 15:30 |
Tokyo | IIS, Univ. of Tokyo | [Poster Presentation]
SVM with weight learning for kernel parameters of each sample Naoya Inoue, Yukihiko Yamashita (TOKYO TECH) IBISML2010-95 |
In the support vector machine (SVM) with an asymmetric kernel function, two mappings in the inner product to a high-dime... [more] | IBISML2010-95 pp.265-270 |
| IBISML, PRMU, IPSJ-CVIM [detail] | 2010-09-05 09:00 |
Fukuoka | Fukuoka Univ. | Behavior of kernel mutual subspace method with respect to parameters Seiji Hotta (TUAT), Tomokazu Kawahara, Osamu Yamaguchi (Toshiba Corp.), Hitoshi Sakano (NTT) PRMU2010-57 IBISML2010-29 |
Optimizing parameters of kernel methods is an unsolved problem. We report the experimental evaluation and the considerat... [more] | PRMU2010-57 IBISML2010-29 pp.1-6 |
| PRMU | 2007-12-13 11:40 |
Hyogo | Kobe Univ. | [Special Talk]
(not registered) Shigeo Abe (Kobe Univ.) PRMU2007-139 |
Support vector machines (SVM) have been attracting much attention because of their high generalization ability for a wid... [more] | PRMU2007-139 p.25 |
| SIS | 2007-12-11 12:45 |
Hyogo | On the SIRMs Connected Fuzzy Reasoning Method Using Kernel Hirosato Seki (Osaka Univ.), Fuhito Mizuguchi (Kronos), Satoshi Watanabe, Hiroaki Ishii (Osaka Univ.), Masaharu Mizumoto (Osaka Electro-Communication Univ.) SIS2007-63 |
Single Input Rule Modules connected fuzzy reasoning method (SIRMs method, for short) by Yubazaki can decrease the number... [more] | SIS2007-63 pp.29-34 |
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