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 Results 1 - 8 of 8  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2022-06-28
13:55
Okinawa
(Primary: On-site, Secondary: Online)
Feature selection in prediction model by LiNGAM
Taiyu Sumida, Takashi Takekawa (Kogakuin Univ.) NC2022-17 IBISML2022-17
To improve the accuracy of machine learning models, it is important to perform feature engineering based on the features... [more] NC2022-17 IBISML2022-17
pp.123-128
MVE, IMQ, IE, CQ
(Joint) [detail]
2021-03-03
14:40
Online Online Semantic and Quantitative Explanation for Networks using Feature Interaction
Bohui Xia, Xueting Wang, Toshihiko Yamasaki (The Univ. of Tokyo) IMQ2020-35 IE2020-75 MVE2020-67
Deep learning-based methods have shown remarkable performances in many tasks. However, it is hard for us to interpret th... [more] IMQ2020-35 IE2020-75 MVE2020-67
pp.127-132
HCS 2020-03-05
10:00
Online Online Meeting [Online] Differences in smartphone dependency trends based on subjective evaluation and individual characteristics in daily life
Koji Mishima (Chubu Univ.), Masayuki Kurokawa (Aichi Univ.of Education), Masatoshi Nagata, Tatsuya Konishi, Toshitaka Hamamura, Masaru Honjo, Akio Yoneyama (KDDI Research, Inc.) HCS2019-92
We conducted an internet survey of approximately 3,000 high school students regarding subjective evaluation in daily lif... [more] HCS2019-92
pp.41-45
ET 2017-10-21
14:10
Fukuoka Kyusyu Institute of Technology (Tobata Campus) Collaborative learning environment on the meaQs system in lecture at the university -- An interaction between class condition and system use --
Masahiko Okamoto, Atsuhiro Kojima (Osaka Pref. Univ.), Akikumo Nakamura (Ruffnote), Akira Notsu, Tomoko Tajima, Kunihito Tibia (Osaka Pref. Univ.) ET2017-46
In this paper we report an interaction between meaQs system and class condition. Our previous study showed that the meaQ... [more] ET2017-46
pp.33-36
SIP, CAS, MSS, VLD 2017-06-19
13:00
Niigata Niigata University, Ikarashi Campus [Invited Talk] Composite Variables and Ensemble: Introduction to Forest Regression and Additive Models
Ichigaku Takigawa (Hokkaido Univ.) CAS2017-8 VLD2017-11 SIP2017-32 MSS2017-8
Machine learning, supervised machine learning in particular, now becomes one of daily tools in signal processing such as... [more] CAS2017-8 VLD2017-11 SIP2017-32 MSS2017-8
p.43
HIP, ITE-HI, ITE-CE [detail] 2016-12-21
14:00
Miyagi Reserach Institute of Electrical Communication, Tohoku University Interaction among sweetness, sourness and lemon flavor on sensory evaluation of lemon-flavor beverages
Hiroaki Maruyama (Tohoku Univ./Sapporo Holdings Ltd.), Shigeki Araki (Sapporo Holdings Ltd.), Nobuyuki Sakai (Tohoku Univ.) HIP2016-71
The taste of the foods and beverages is perceptual experience that consists of integration of several senses, such as gu... [more] HIP2016-71
pp.49-52
NC, IPSJ-BIO, IBISML, IPSJ-MPS
(Joint) [detail]
2015-06-23
14:40
Okinawa Okinawa Institute of Science and Technology Selective inference for high-order interaction model
Shinya Suzumura, Kazuya Nakagawa (NIT), Koji Tsuda (UT), Ichiro Takeuchi (NIT) IBISML2015-11
Finding statistically significant high-order interaction features in predictive modeling is important but challenging ta... [more] IBISML2015-11
pp.69-74
HIP 2008-12-18
- 2008-12-19
Miyagi RIEC Spatial perception of audio-visual information in peripheral vision
Dae-Gee Kang, Royta Miyauchi, Yukio Iwaya, Yo-iti Suzuki (Tohoku Univ.) HIP2008-145
Psychophysical experiment was conducted to investigate the impact of the audio-visual spatial disparity on perceptual sp... [more] HIP2008-145
pp.117-122
 Results 1 - 8 of 8  /   
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