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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU | 2020-12-17 16:30 |
Online | Online | Towards Discovery of Relevant Latent Factors with Limited Data Mohit Chhabra, Quan Kong, Tomoaki Yoshinaga (Hitachi)  PRMU2020-49 |
The remarkable effectiveness of neural networks on vision tasks has led to an interest in adapting neural network models... [more] | PRMU2020-49 pp.63-68 |
| SIS | 2019-12-12 14:55 |
Okayama | Okayama University of Science | Machine learning algorithms with quantized images and their influence Takayuki Osakabe, Yuma Kinoshita, Hitoshi Kiya (Tokyo Metro.Univ.) SIS2019-27 |
Recently, appling quantized images to machine learning algorithms is expected to enhance robustness against adversarial... [more] |
SIS2019-27 pp.23-28 |
| SIP, EA, SP, MI (Joint) [detail] |
2018-03-19 14:45 |
Okinawa | Optimization of Gaussian Kernel Parameters for Kernel Logistic Regression Kosuke Fukumori, Tomoya Wada, Toshihisa Tanaka (TUAT) EA2017-135 SIP2017-144 SP2017-118 |
The kernel logistic regression is a nonlinear classification model that effectively uses kernel methods, which are one o... [more] | EA2017-135 SIP2017-144 SP2017-118 pp.185-190 |
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| IBISML | 2017-03-07 11:30 |
Tokyo | Tokyo Institute of Technology | A stochastic optimization method and generalization bounds for voting classifiers by continuous density functions Atsushi Nitanda (Tokyo Tech./NTTDATA MSI), Taiji Suzuki (Tokyo Tech./JST/RIKEN) IBISML2016-108 |
We consider a learning method for the majority vote classifier by probability measure on continuously parametrized space... [more] | IBISML2016-108 pp.63-69 |
| 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 |
| MBE, NC (Joint) |
2016-03-22 15:20 |
Tokyo | Tamagawa University | Quantitative analysis on push/stroke operation by a finger to real-world objects Masayuki Senoo, Megumi Nakao, Tetsuya Matsuda (Kyoto Univ.) MBE2015-113 |
Finger manipulation analysis is an active area of research for developing medical training system and for designing dext... [more] | MBE2015-113 pp.55-60 |
| IBISML | 2015-11-26 15:00 |
Ibaraki | Epochal Tsukuba | [Poster Presentation]
Theoretical Vulnerability Evaluation on Linear Classifier under Self-Information Controllable Settings Shohei Kobayashi (Univ. of Tsukuba), Shota Okumura, Ichiro Takeuchi (nitech), Jun Sakuma (Univ. of Tsukuba) IBISML2015-64 |
Self-information controllable learning is machine learning algorithms that allow data providers to control their data ev... [more] | IBISML2015-64 pp.83-90 |
| NC, MBE (Joint) |
2014-03-18 13:50 |
Tokyo | Tamagawa University | Effectiveness of scalp-hemodynamic reduction to motor-task classification of functional near-infrared spectroscopy signals Takanori Sato, Kyoko Sugai, Isao Nambu, Yasuhiro Wada (Nagaoka Univ. of Tech.) MBE2013-141 |
Functional near-infrared spectroscopy (fNIRS) has been considered the application to brain-computer interfaces (BCIs) in... [more] | MBE2013-141 pp.145-150 |
| SP | 2014-01-23 16:00 |
Aichi | Meijo Univ. | Speaker recognition based on log-linear models using feature generation by variational Bayesian method Akifumi Tsuge, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) SP2013-98 |
This paper presents a speaker recognition technique based on log-linear models (LLMs) using Bayesian statistics. Since d... [more] | SP2013-98 pp.13-18 |
| IBISML | 2013-11-12 15:45 |
Tokyo | Tokyo Institute of Technology, Kuramae-Kaikan | [Poster Presentation]
Exaluation of Revised IP-OLDF with S-SVM, LDF and logistic regression by K-fold cross-validation Shuichi Shinmura (Seikei Univ.) IBISML2013-44 |
In this paper, Revised IP-OLDF based on MNM criterion is proposed using a mixed integer programming. The new discriminan... [more] | IBISML2013-44 pp.61-68 |
| SIP | 2013-08-29 16:10 |
Tokyo | Tokyo University of Agriculture and Technology | [Tutorial Lecture]
Tensor-Based Machine Learning: Modeling, Algorithms and Applications Qibin Zhao, Andrzej Cichocki (RIKEN) SIP2013-73 |
Tensors are a generalization of vectors and matrices to higher dimensions that can naturally represent the multidimensio... [more] | SIP2013-73 pp.35-40 |
| IBISML | 2012-06-19 10:30 |
Kyoto | Campus plaza Kyoto | Learning Non-Linear Classifiers with a Sparsity Upper-Bound via Efficient Model Selection Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara (Kobe Univ.) IBISML2012-2 |
Support Vector Machines, when combined with kernels, achieve state-of-the-art accuracy on many datasets. However, their... [more] |
IBISML2012-2 pp.9-14 |
| IBISML, PRMU, IPSJ-CVIM [detail] | 2010-09-06 13:40 |
Fukuoka | Fukuoka Univ. | [Fellow Memorial Lecture]
- Takio Kurita (Hiroshima Univ.) PRMU2010-84 IBISML2010-56 |
Linear Discriminant Analysis (LDA) is one of the well known methods to extract good features for classification. Otsu de... [more] | PRMU2010-84 IBISML2010-56 pp.209-214 |
| PRMU | 2005-10-27 14:15 |
Miyagi | Tohoku Univ. | Face Recognition by Self Quotient Image based on Classification of Surface Appearance Masashi Nishiyama, Osamu Yamaguchi (TOSHIBA) |
In this paper we present a new method for synthesizing illumination invariant images for face recognition. The method cl... [more] | PRMU2005-89 pp.33-38 |
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