IEICE TRANSACTIONS on Fundamentals
Authors' Reply to the Comments by Kamata et al.
Summary :
We thank Kamata et al. (2023) [1] for their interest in our work [2], and for providing an explanation of the quasi-linear kernel from a viewpoint of multiple kernel learning. In this letter, we first give a summary of the quasi-linear SVM. Then we provide a discussion on the novelty of quasi-linear kernels against multiple kernel learning. Finally, we explain the contributions of our work [2].
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E106-A No.11 pp.1446-1449
- Publication Date
- 2023/11/01
- Publicized
- 2023/05/08
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2023EAL2006
- Type of Manuscript
- WRITTEN DISCUSSION
- Category
Authors
Bo ZHOU
Xi'an Jiaotong University
Benhui CHEN
Dali University
Jinglu HU
Waseda University
Keyword
Latest Issue
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Bo ZHOU, Benhui CHEN, Jinglu HU, "Authors' Reply to the Comments by Kamata et al." in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 11, pp. 1446-1449, November 2023, doi: 10.1587/transfun.2023EAL2006.
Abstract: We thank Kamata et al. (2023) [1] for their interest in our work [2], and for providing an explanation of the quasi-linear kernel from a viewpoint of multiple kernel learning. In this letter, we first give a summary of the quasi-linear SVM. Then we provide a discussion on the novelty of quasi-linear kernels against multiple kernel learning. Finally, we explain the contributions of our work [2].
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2023EAL2006/_p
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@ARTICLE{e106-a_11_1446,
author={Bo ZHOU, Benhui CHEN, Jinglu HU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Authors' Reply to the Comments by Kamata et al.},
year={2023},
volume={E106-A},
number={11},
pages={1446-1449},
abstract={We thank Kamata et al. (2023) [1] for their interest in our work [2], and for providing an explanation of the quasi-linear kernel from a viewpoint of multiple kernel learning. In this letter, we first give a summary of the quasi-linear SVM. Then we provide a discussion on the novelty of quasi-linear kernels against multiple kernel learning. Finally, we explain the contributions of our work [2].},
keywords={},
doi={10.1587/transfun.2023EAL2006},
ISSN={1745-1337},
month={November},}
Copy
TY - JOUR
TI - Authors' Reply to the Comments by Kamata et al.
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1446
EP - 1449
AU - Bo ZHOU
AU - Benhui CHEN
AU - Jinglu HU
PY - 2023
DO - 10.1587/transfun.2023EAL2006
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2023
AB - We thank Kamata et al. (2023) [1] for their interest in our work [2], and for providing an explanation of the quasi-linear kernel from a viewpoint of multiple kernel learning. In this letter, we first give a summary of the quasi-linear SVM. Then we provide a discussion on the novelty of quasi-linear kernels against multiple kernel learning. Finally, we explain the contributions of our work [2].
ER -
English
