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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 8 of 8  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
DE, IPSJ-DBS, IPSJ-IFAT 2024-12-26
13:30
Online Online (Online) DE2024-14 There are soft clustering methods such as Fuzzy C-means that take into account the possibility of multiple labels belong... [more] DE2024-14
pp.13-18
PRMU, IPSJ-CVIM, IPSJ-DCC, IPSJ-CGVI 2023-11-16
16:50
Tottori (Tottori, Online)
(Primary: On-site, Secondary: Online)
On Food Plant Classification from Luehdorfia Japonica Images using Multi-label Classification ABN
Tsubasa Hirakawa, Takaaki Arai, Takayoshi Yamashita, Hironobu Fujiyoshi, Yuichi Oba, Hiromichi Fukui (Chubu Univ.), Masaya Yago (Tokyo Univ.) PRMU2023-27
Butterfly is a familiar taxon. Because of the abundance of specimens and the ease of comparison between specimens, regio... [more] PRMU2023-27
pp.62-67
NLC, IPSJ-ICS 2022-07-08
15:55
Online Online (Online) Topic Classification of Kyutech Corpus by Machine Learning
Shinnosuke Kawasaki, Kazutaka Shimada (Kyutech) NLC2022-3
Discussion summarization is one of the most important tasks for discussion analysis.
Utterances in a discussion contain... [more]
NLC2022-3
pp.13-18
NLC, IPSJ-NL, SP, IPSJ-SLP [detail] 2021-12-03
16:10
Online Online (Online) Label smoothing with co-occurrences information for multi-label classification
Yuki Yasuda, Taichi Ishiwatari, Taro Miyazaki, Jun Goto (NHK) NLC2021-27 SP2021-48
Imbalanced learning is one of the big issues in multi-label classification task. Training models using such imbalanced d... [more] NLC2021-27 SP2021-48
pp.48-53
MI 2021-03-16
09:15
Online Online (Online) Tuberculosis in Chest CT Image Analysis based on multi-axis projections using Deep learning
Tetsuya Asakawa, Masaki Aono (Toyohashi Univ) MI2020-64
The purpose of this research is to make accurate estimates for the six labels (Left affected, Right affected, Light ple... [more] MI2020-64
pp.74-79
SP 2019-08-28
17:00
Kyoto Kyoto Univ. (Kyoto) Speech Emotion Classification based on Multi-Label Emotion Existence Estimation
Atsushi Ando, Ryo Masumura, Hosana Kamiyama, Satoshi Kobashikawa, Yushi Aono (NTT) SP2019-16
This paper presents a novel speech emotion classification that addresses the ambiguous nature of emotions in speech. Mos... [more] SP2019-16
pp.39-44
PRMU 2017-10-12
14:30
Kumamoto (Kumamoto) Generalized Subclass Method for Multi-label Classification
Batzaya Norov-Erdene, Mineichi Kudo (Hokkaido Univ.) PRMU2017-74
Multi-label classification (MLC) problems are emerging in medical diagnosis, web page annotation, image annotation, etc.... [more] PRMU2017-74
pp.67-72
ITS, IE, ITE-AIT, ITE-HI, ITE-ME, ITE-MMS, ITE-CE [detail] 2016-02-23
10:15
Hokkaido Hokkaido Univ. (Hokkaido) Refinement of Multi-Label Classification Results using Class Relations Based on Higher-Order MRF
Ryosuke Furuta, Yusuke Fukushima, Toshihiko Yamasaki, Kiyoharu Aizawa (UT) ITS2015-76 IE2015-118
In multi-label classi cation problems, in which multiple labels are estimated for an input data, the simplest way is to ... [more] ITS2015-76 IE2015-118
pp.241-246
 Results 1 - 8 of 8  /   
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