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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | [Poster Presentation]
Optimization Method of Deep Ensemble Learning using Hierarchical Clustering Natsuki Koda, Sumio Watanabe (Tokyo Tech)  IBISML2016-70 |
The method which is used for prediction by combining many different learning machines generated by using same training d... [more] | IBISML2016-70 pp.171-176 |
| NC, NLP (Joint) |
2016-01-28 15:25 |
Fukuoka | Kyushu Institute of Technology | Validation of the Effects of Ensemble Learning for i-vector-based Speaker Identification
-- Bagging vs Random forest -- Shohei Sonoda, Masato Inoue (Waseda Univ) NC2015-58 |
Currently, most speaker identification methods have been performed by i-vectors which represent the features of unique s... [more] | NC2015-58 pp.13-16 |
| IBISML | 2015-11-27 14:00 |
Ibaraki | Epochal Tsukuba | [Poster Presentation]
Recursive Ensemble Land Cover Classification for Few Training Data and Many Class Yu Oya, Katsutoshi Kanamori, Hayato Ohwada (TUS) IBISML2015-77 |
Many global and environmental applications require land use and land cover information. A land cover classification is o... [more] | IBISML2015-77 pp.183-188 |
| MBE, NC (Joint) |
2014-12-13 14:00 |
Aichi | Nagoya University | An ensemble learning system for correcting user's skills and its application for education Takaya Ogiso, Koichiro Yamauchi (Chubu Univ) NC2014-53 |
In these days, artificial intelligence systems are well used to solve various problems in our life. However, these arti... [more] |
NC2014-53 pp.55-58 |
| IT | 2014-07-17 10:10 |
Hyogo | Kobe University | Distance Metric Learning with Low Computational Complexity based on Ensemble of Low-dimensional Matrixes Hiroshi Saito, Fumihiro Yamazaki, Kenta Mikawa, Masayuki Goto (Waseda Univ.) IT2014-12 |
The distance metric learning is the approach which enables to acquire a good metric for automatic data classification. I... [more] | IT2014-12 pp.7-12 |
| IT | 2014-07-18 10:20 |
Hyogo | Kobe University | A Prediction Method based on Weighted Ensemble of Decision Tree on Alternating Decision Forests. Shotaro Misawa, Naohiro Fujiwara, Kenta Mikawa, Masayuki Goto (Waseda Univ.) IT2014-29 |
In this study, we focus on the Alternating Decision Forests (ADF). The ADF introduces the weights which represent the de... [more] | IT2014-29 pp.101-106 |
| MI | 2014-01-26 13:30 |
Okinawa | Bunka Tenbusu Kan | Multi-organ localizations on a large number of CT images by using machine learning and its performance evaluations Shoichi Morita, Xiangrong Zhou, Huayue Chen, Takeshi Hara (Gifu Univ.), Huiyan Jiang (NEU), Ryujiro Yokoyama (Gifu Univ.), Masayuki Kanematsu (Gifu Univ. Hospital), Hiroaki Hoshi, Hiroshi Fujita (Gifu Univ.) MI2013-62 |
In this study, we propose an approach to accomplish general localization of the different inner organ regions on 3D CT s... [more] | MI2013-62 pp.37-41 |
| SIS | 2013-12-12 15:00 |
Tottori | Torigin Bunka Kaikan (Tottori) | Effects of input patterns including noise on generalization of neural network systems with ensemble learning Akihiro Tanaka, Satoru Kishida (Tottori Univ.) SIS2013-40 |
We produced input patterns including noise and investigated the effect of them on the generalization of neural network s... [more] | SIS2013-40 pp.71-74 |
| IBISML | 2013-11-12 15:45 |
Tokyo | Tokyo Institute of Technology, Kuramae-Kaikan | [Poster Presentation]
A boosting method considering tolerance against noisy data by weighting each data according to the distance between incidents Shinjiro Fujita, Sayaka Kamei, Satoshi Fujita (Hiroshima Univ.) IBISML2013-38 |
AdaBoost is one of the major ensemble learning methods. It is easy to implement and has high classification accuracy. ... [more] |
IBISML2013-38 pp.15-21 |
| NC | 2012-07-30 11:35 |
Shiga | Ritsumeikan Univ. College of Information Science and Engineering | Noise reduction for images by ensemble learning Eiji Watanabe (Konan Univ.), Takashi Ozeki (Fukuyama Univ.), Takeshi Kohama (Kinki Univ.) NC2012-16 |
This report discusses a restoration method for JPEG images based on ensemble learning algorithm for multiple multi-laye... [more] |
NC2012-16 pp.13-18 |
| AI | 2011-11-21 13:00 |
Fukuoka | An Imputation of context data by using Random Forest Tsunenori Ishioka (NCUEE) AI2011-21 |
When considering contextware services, we set the response variable to the services to provide, and explanatory variable... [more] | AI2011-21 pp.25-30 |
|
| CAS, NLP | 2011-10-20 10:00 |
Shizuoka | Shizuoka Univ. | Evaluating the Risk of Nonlinear Prediction with the Bagging Algorithm Kazuya Nakata, Tomoya Suzuki (Ibaraki Univ.) CAS2011-33 NLP2011-60 |
Some real phenomena are derived from unstationary systems, and therefore we have to select recent historical data which ... [more] | CAS2011-33 NLP2011-60 pp.1-6 |
| IBISML, PRMU, IPSJ-CVIM [detail] | 2010-09-05 16:00 |
Fukuoka | Fukuoka Univ. | Facial Expression Recognition by Ensemble Learning Using Movements of Facial Feature Points Hiroki Nomiya, Teruhisa Hochin (KIT) PRMU2010-68 IBISML2010-40 |
We propose a facial expression recognition method using several facial feature points such as end points and centroids o... [more] | PRMU2010-68 IBISML2010-40 pp.85-92 |
| AI | 2010-06-25 14:15 |
Tokyo | Refining Noisy Training Examples Based on Ensemble Learning for Intelligent Domain-Specific WEB Search Hiroki Hirabayashi, Koji Iwanuma, Yoshitaka Yamamoto, Hidetomo Nabeshima (Univ. of Yamanashi) AI2010-5 |
The Keyword Spices, proposed Oyama et al., is a sort of a query-expansion technology, which adds pre-computed additional... [more] | AI2010-5 pp.25-30 |
|
| NS, IN (Joint) |
2010-03-05 11:00 |
Miyazaki | Miyazaki Phoenix Seagaia Resort (Miyazaki) | Unsupervised Ensemble Anomaly Detection Method using Time-Periodical Packet Sampling Shuichi Nawata, Masato Uchida (Kyushu Inst. of Tech.), Yu Gu (NEC Labs America), Masato Tsuru, Yuji Oie (Kyushu Inst. of Tech.) IN2009-198 |
We propose an anomaly detection method that trains a baseline model describing the normal behavior of network traffic wi... [more] | IN2009-198 pp.325-330 |
| NC, MBE (Joint) |
2008-03-13 14:10 |
Tokyo | Tamagawa Univ | Policy gradient method for a policy function with probabilistic parameters Yutaka Nakamura (Osaka Univ.) NC2007-170 |
Stochastic policy gradient methods are a type of reinforcement learning method, where the parameter of the policy parame... [more] | NC2007-170 pp.343-348 |
| NC | 2006-06-16 10:10 |
Okinawa | OIST | A Method of Data Classification of Bagging Using HRGA/P and Its Applications Hong Zhang, Masumi Ishikawa (K.I.T.) |
To obtain a classification model with high generalization ability, we propose to use a hybrid real-coded genetic algorit... [more] | NC2006-25 pp.19-24 |
| PRMU, TL | 2006-02-24 16:30 |
Ibaraki | A Weighted Voting Method to accelerate Writer Adaptation for On-line Handwriting Recognition Akira Nakamura (SANYO Electric) |
An approach to accelerate writer adaptation for on-line handwriting recognition is described. It is known that adapting ... [more] | TL2005-89 PRMU2005-224 pp.129-134 |
|
| NC | 2006-01-24 09:00 |
Hokkaido | Hokkaido Univ. | Adaptive Classifiers-Ensemble System for Concept-Drifting Environments Kyosuke Nishida, Koichiro Yamauchi, Takashi Omori (Hokkaido Univ.) |
Most machine learning algorithms assume stationary environments, require a large number of training examples in advance,... [more] | NC2005-98 pp.1-6 |
| NLP | 2005-11-18 13:25 |
Fukuoka | Kyushu Institute of Technology | Ensemble Self-Generating Neural Networks for Chaotic Time Series Prediction Masaki Nakahara, Hirotaka Inoue (KNCT) |
In this paper,we present a performanse characteristic of self-generating neural networks(SGNNs) applied to time series ... [more] |
NLP2005-63 pp.7-12 |
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