|
|
All Technical Committee Conferences (Searched in: All Years)
|
|
Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
|
Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
CAS, SIP, VLD, MSS |
2022-06-16 10:25 |
Aomori |
Hachinohe Institute of Technology (Primary: On-site, Secondary: Online) |
Equal Opportunity in Robust Optimization for Unit Commitment Problem Considering Suppression of Renewable Energy Ichiro Toyoshima (TOSHIBA ESS), Pierre-Louis Poirion (RIKEN AIP), Tomohide Yamazaki, Kota Yaguchi, Masayuki Kubota, Ryota Mizutani (TOSHIBA ESS), Akiko Takeda (The University of Tokyo) CAS2022-2 VLD2022-2 SIP2022-33 MSS2022-2 |
[more] |
CAS2022-2 VLD2022-2 SIP2022-33 MSS2022-2 pp.7-12 |
IBISML |
2020-10-20 14:40 |
Online |
Online |
IBISML2020-14 |
[more] |
IBISML2020-14 p.30 |
IBISML |
2019-03-06 11:00 |
Tokyo |
RIKEN AIP |
Shapelet-based Multiple-Instance Learning Daiki Suehiro, Kohei Hatano (Kyushu Univ./RIKEN AIP), Eiji Takimoto (Kyushu Univ.), Shuji Yamamoto, Kenichi Bannai (Keio Univ./RIKEN AIP), Akiko Takeda (The Univ. of Tokyo/RIKEN AIP) IBISML2018-112 |
[more] |
IBISML2018-112 pp.51-58 |
MSS, CAS, IPSJ-AL [detail] |
2018-11-12 17:15 |
Shizuoka |
|
[Invited Talk]
Robust Optimization and its Application to Supervised Learning Akiko Takeda (U.Tokyo) CAS2018-67 MSS2018-43 |
There are various uncertainties in real-world problems. When formulating them as mathematical optimization problems, we ... [more] |
CAS2018-67 MSS2018-43 p.55 |
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 |
IBISML |
2015-11-26 15:00 |
Ibaraki |
Epochal Tsukuba |
[Poster Presentation]
Robustification of Learning Algorithms using Hinge-loss Takafumi Kanamori (Nagoya Univ.), Shuhei Fujiwara (TopGate), Akiko Takeda (Univ. of Tokyo) IBISML2015-71 |
We propose a unified formation of robust learning methods for classification and regression problems.
In the learnin... [more] |
IBISML2015-71 pp.139-146 |
IBISML |
2015-11-27 14:00 |
Ibaraki |
Epochal Tsukuba |
[Poster Presentation]
An Efficient Accelerated Proximal Gradient Method for Unified Binary Classification Model Naoki Ito, Akiko Takeda (UTokyo), Kim-Chuan Toh (NUS) IBISML2015-93 |
In this paper, we develop an efficient general optimization algorithm for a unified formulation of various binary classi... [more] |
IBISML2015-93 pp.299-303 |
IBISML |
2014-11-17 17:00 |
Aichi |
Nagoya Univ. |
[Poster Presentation]
Breakdown Point of Robust Support Vector Machine Takafumi Kanamori (Nagoya Univ.), Shuhei Fujiwara, Akiko Takeda (Univ. of Tokyo) IBISML2014-41 |
The support vector machine (SVM) is one of the most successful learning methods for solving classification
problems. D... [more] |
IBISML2014-41 pp.49-56 |
IBISML |
2014-11-17 17:00 |
Aichi |
Nagoya Univ. |
[Poster Presentation]
Exact SVM Training by Wolfe's Minimum Norm Point Algorithm Masashi Kitamura, Akiko Takeda, Satoru Iwata (Univ. of Tokyo) IBISML2014-43 |
(Advance abstract in Japanese is available) [more] |
IBISML2014-43 pp.65-71 |
IBISML |
2013-11-12 15:45 |
Tokyo |
Tokyo Institute of Technology, Kuramae-Kaikan |
[Poster Presentation]
Global Solvers for Variational Bayesian Low-rank Subspace Clustering Shinichi Nakajima (Nikon), Akiko Takeda (Univ. of Tokyo), S. Derin Babacan (Google), Masashi Sugiyama (Tokyo Inst. of Tech.), Ichiro Takeuchi (Nagoya Inst. of Tech.) IBISML2013-37 |
Variational Bayesian (VB) learning, known to be a promising approximation method to Bayesian learning,
is generally per... [more] |
IBISML2013-37 pp.7-14 |
IBISML |
2011-03-28 16:50 |
Osaka |
Nakanoshima Center, Osaka Univ. |
Enumerating Feature-Sets with Submodularity Yoshinobu Kawahara (Osaka Univ.), Koji Tsuda (AIST), Takashi Washio (Osaka Univ.), Akiko Takeda (Keio Univ.), Shin-ichi Minato (Hokkaido Univ.) IBISML2010-113 |
Selecting relevant features is a fundamental task in machine learning. Although many approaches have been investigated s... [more] |
IBISML2010-113 pp.63-68 |
|
|
|
Copyright and reproduction :
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
|
[Return to Top Page]
[Return to IEICE Web Page]
|