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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. | Extraction of Cluster Structural Changes using Variational Bayes Daisuke Kaji (Denso), Kazuho Watanabe (Toyohashi Tech.)  IBISML2016-78 |
Variational Bayes learning (VB) is widely applied to clustering problems as the low computational cost algorithm of Baye... [more] | IBISML2016-78 pp.229-233 |
| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. | [Poster Presentation]
Stochastic Particle Gradient Descent for the Infinite Majority Vote Classifier Atsushi Nitanda, Taiji Suzuki (Tokyo Tech.) IBISML2016-79 |
We consider a learning method for the infinite majority vote classifier combined by a density on a continuous space of b... [more] | IBISML2016-79 pp.235-241 |
| DC, SS | 2016-10-28 11:45 |
Shiga | Hikone Kinro-Fukushi Kaikan Bldg. | A Study of the Growth of Programmers with Online Judge Archives Yusaku Noriyuki, Takao Nakagawa, Hideaki Hata, Kenichi Matsumoto (NAIST) SS2016-34 DC2016-36 |
An online judge is an online system that provides programming problems and an environment for compiling and testing subm... [more] | SS2016-34 DC2016-36 pp.97-101 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2016-07-05 13:00 |
Okinawa | Okinawa Institute of Science and Technology | Non-linear Embedded Feature Extraction Method using Comb-shaped Neural Network Akihito Sudo (UT), Tomoyuki Higuchi, Shin'ya Nakano, Masaya Saito (ISM), Takahiro Yabe, Yoshihide Sekimoto (UT) IBISML2016-1 |
Feature selection methods can be divided into three categories; wrapper methods, filter methods, and embedded methods. E... [more] | IBISML2016-1 pp.127-131 |
| ET, IPSJ-CLE | 2016-05-21 10:15 |
Nagano | Nagano (department of technology) campus, Shinshu Univ. | Advantages of instructions in audio for note-taking and test scores through a fully online course Minoru Nakayama (Tokyo Tech.), Kouichi Mutsuura, Hiroh Yamamoto (Shinshu Univ.) ET2016-1 |
Lexical analysis was conducted for slide information, audio information and student's note taking activity in order to... [more] |
ET2016-1 pp.43-48 |
| PRMU, BioX | 2016-03-25 15:45 |
Tokyo | Efficient Mondrian Forests by Introducing Supervised Learning Ryuei Murata (Chubu Univ.), Akisato Kimura, Yoshitaka Ushiku (NTT), Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi (Chubu Univ.) BioX2015-73 PRMU2015-196 |
Mondrian Forests is an online learning method based on framework of Random Forests. At the online learning, Mondrian For... [more] | BioX2015-73 PRMU2015-196 pp.191-196 |
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| ET | 2016-03-05 14:00 |
Kagawa | Kawaga Univ. (Saiwai-cho Campus) | Construction and Evaluation of Programing Learning Environment Using Online Judge System Yuuki Furutani, Kazuhiko Nagao, Sayaka Minewaki (NIT, Yuge College) ET2015-101 |
In universities and colleges, computer programming subject is often learnt by completing coding assignments. However, t... [more] |
ET2015-101 pp.45-50 |
| NC, NLP (Joint) |
2016-01-29 15:25 |
Fukuoka | Kyushu Institute of Technology | Statistical Mechanics of Perceptron Learning with Noisy Teacher Arata Honda, Kazushi Ikeda (NAIST) NC2015-65 |
Learning curves of simple perceptron were derived here. They have been analyzed for half a century and the learning curv... [more] | NC2015-65 pp.45-48 |
| NC, NLP (Joint) |
2016-01-29 16:15 |
Fukuoka | Kyushu Institute of Technology | Proposal of novel dropout method and its analysis of dynamic property Daisuke Saitoh, Tasuku Kondo, Kazuyuki Hara (Nihon Univ.) NC2015-67 |
Deep learning that use a large network and includes many units tends to occur the overfitting. Therefore, to avoid the o... [more] | NC2015-67 pp.55-60 |
| MI | 2016-01-20 14:26 |
Okinawa | Bunka Tenbusu Kan | Estimation of Liver Deformation Using Real-Time Nonlinear Finite Element Method by Deep Neural Network Kaoru Kobayashi, Ken'ichi Morooka (Kyushu Univ.), Yasushi Miyagi (Kaizuka Hospital), Takaichi Fukuda (Kumamoto Univ.), Tokuo Tsuji, Ryo Kurazume (Kyushu Univ.), Kazuhiro Samura (Fukuoka Univ.) MI2015-138 |
This paper proposes a real-time nonlinear nite element method (FEM) for estimating soft tissue deformations by deep neu... [more] | MI2015-138 pp.321-325 |
| EMM | 2016-01-18 14:00 |
Miyagi | Katahira Campus, Tohoku University | An Online Education System Based on Information Hiding Techniques Yuki Sada, Yui Ichioka, Toru Tachikawa, Tetsuya Kojima (NIT, Tokyo College) EMM2015-63 |
Online courses have been quite popular these days. Students can watch and read the learning materials regardless of time... [more] | EMM2015-63 pp.13-18 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2015-09-15 15:00 |
Ehime | Proposal of selection of training data using misdetected goodware for preventing misdetection of a static detector of malware Yasushi Okano, Atsutoshi Kumagai, Masaki Tanikawa, Yoshihito Oshima (NTT), Kenji Aiko, Kazumi Umehashi, Junichi Murakami (FFRI) PRMU2015-90 IBISML2015-50 |
A lot of variant and new malware is produced day by day, it is therefore the urgent need to countermeasure such as "unkn... [more] | PRMU2015-90 IBISML2015-50 pp.163-170 |
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| NC, IPSJ-BIO, IBISML, IPSJ-MPS (Joint) [detail] |
2015-06-23 15:45 |
Okinawa | Okinawa Institute of Science and Technology | A Bridge between Hedge and Exp3 Algorithms Atsuyoshi Nakamura (Hokkaido Univ.) IBISML2015-13 |
Hedge is an online learning algorithm that draws an expert according to a probability distribution which depends on the ... [more] | IBISML2015-13 pp.81-86 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS (Joint) [detail] |
2015-06-23 16:10 |
Okinawa | Okinawa Institute of Science and Technology | Optimal Algorithms in Dueling Bandit Problem Junpei Komiyama, Junya Honda (U-Tokyo), Hisashi Kashima (Kyoto University), Hiroshi Nakagawa (U-Tokyo) IBISML2015-14 |
We study the K-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is... [more] | IBISML2015-14 pp.87-94 |
| SS | 2015-03-10 10:35 |
Okinawa | OKINAWAKEN SEINENKAIKAN | Integrating Online Presentation and Courseware Production to Support Higher Education on the WebELS Platform Mohamed Osamnia (SOKENDAI), Arjulie John Berena, Hitoshi Okada, Haruki Ueno (NII) SS2014-71 |
In higher education, the teaching methodologies are changing from the classroom-based methodology to the so-called Virtu... [more] | SS2014-71 pp.97-102 |
| IPSJ-AVM, CS, IE, ITE-BCT [detail] | 2014-12-04 15:40 |
Osaka | Osaka University Nakanoshima Center | human detection from low quality Aerial images using Deep Learning and Online Learning Takayuki Fujimura, Daisuke Sugimura (TUS), Fumie Ono, Ryu Miura (NICT), Takayuki Hamamoto (TUS) CS2014-75 IE2014-61 |
We propose a method for human detection from Aerial Images taken by Unmanned Aerial Vehicle (UAV). In that situations, t... [more] | CS2014-75 IE2014-61 pp.31-34 |
| IBISML | 2014-11-18 15:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Nonlinear Regression Using Deep Learning Wataru Uchiyama, Toshiyuki Tanaka (Kyoto Univ.) IBISML2014-81 |
In recent years, deep learning has been attracting a great deal of researchers' attention with its performance reported ... [more] | IBISML2014-81 pp.345-349 |
| ET | 2014-07-05 13:05 |
Akita | Akita Univ. | An ID Model for Design and Development of Online Virtual Labs Mohamed Elsayed Ahmed, Shinobu Hasegawa (JAIST) ET2014-22 |
The purpose of this research is to propose a general instructional design (ID) model to teach students in department of ... [more] | ET2014-22 pp.1-5 |
| IBISML | 2014-03-07 10:05 |
Nara | Nara Women's University | Online Prediction with Bradley-Terry Models and Logistic Models Issei Matsumoto, Kohei Hatano, Eiji Takimoto (Kyushu Univ) IBISML2013-74 |
We consider an online density estimation problem under the Bradley-Terry model which determines the probability of the o... [more] | IBISML2013-74 pp.55-62 |
| IBISML | 2014-03-07 10:30 |
Nara | Nara Women's University | Online Matrix Prediction with Log-Determinant Regularizer Kenichiro Moridomi, Kohei Hatano, Eiji Takimoto (Kyushu Univ.), Koji Tsuda (AIST) IBISML2013-75 |
We consider an online symmetric positive semi-definite matrix prediction problem with convex loss function and Frobenius... [more] | IBISML2013-75 pp.63-70 |
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