The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kami mencadangkan tetapan masalah klasifikasi baru di mana Kelas Tidak Diingini (UC) ditakrifkan untuk setiap kelas. UC ialah kelas yang anda mahu elakkan salah klasifikasi. Untuk menangani tetapan ini, kami mencadangkan rangka kerja untuk mengurangkan kebarangkalian untuk UC sambil meningkatkan kebarangkalian untuk kelas yang betul.
Kazuki EGASHIRA
The University of Tokyo
Atsuyuki MIYAI
The University of Tokyo
Qing YU
The University of Tokyo
Go IRIE
Tokyo University of Science
Kiyoharu AIZAWA
The University of Tokyo
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Salinan
Kazuki EGASHIRA, Atsuyuki MIYAI, Qing YU, Go IRIE, Kiyoharu AIZAWA, "Negative Learning to Prevent Undesirable Misclassification" in IEICE TRANSACTIONS on Information,
vol. E107-D, no. 1, pp. 144-147, January 2024, doi: 10.1587/transinf.2023EDL8056.
Abstract: We propose a novel classification problem setting where Undesirable Classes (UCs) are defined for each class. UC is the class you specifically want to avoid misclassifying. To address this setting, we propose a framework to reduce the probabilities for UCs while increasing the probability for a correct class.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8056/_p
Salinan
@ARTICLE{e107-d_1_144,
author={Kazuki EGASHIRA, Atsuyuki MIYAI, Qing YU, Go IRIE, Kiyoharu AIZAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Negative Learning to Prevent Undesirable Misclassification},
year={2024},
volume={E107-D},
number={1},
pages={144-147},
abstract={We propose a novel classification problem setting where Undesirable Classes (UCs) are defined for each class. UC is the class you specifically want to avoid misclassifying. To address this setting, we propose a framework to reduce the probabilities for UCs while increasing the probability for a correct class.},
keywords={},
doi={10.1587/transinf.2023EDL8056},
ISSN={1745-1361},
month={January},}
Salinan
TY - JOUR
TI - Negative Learning to Prevent Undesirable Misclassification
T2 - IEICE TRANSACTIONS on Information
SP - 144
EP - 147
AU - Kazuki EGASHIRA
AU - Atsuyuki MIYAI
AU - Qing YU
AU - Go IRIE
AU - Kiyoharu AIZAWA
PY - 2024
DO - 10.1587/transinf.2023EDL8056
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E107-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2024
AB - We propose a novel classification problem setting where Undesirable Classes (UCs) are defined for each class. UC is the class you specifically want to avoid misclassifying. To address this setting, we propose a framework to reduce the probabilities for UCs while increasing the probability for a correct class.
ER -