TY - JOUR
T1 - Authors’ Reply to the Comments by Kamata et al.
AU - Zhou, Bo
AU - Chen, Benhui
AU - Hu, Jinglu
N1 - Publisher Copyright:
Copyright © 2023 The Institute of Electronics, Information and Communication Engineers.
PY - 2023/11
Y1 - 2023/11
N2 - 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].
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].
KW - classification
KW - machine learning
KW - quasi-linear kernel function
KW - support vector machine
KW - system modeling and parameter estimation
UR - http://www.scopus.com/inward/record.url?scp=85176308308&partnerID=8YFLogxK
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U2 - 10.1587/transfun.2023EAL2006
DO - 10.1587/transfun.2023EAL2006
M3 - Article
AN - SCOPUS:85176308308
SN - 0916-8508
VL - E106.A
SP - 1446
EP - 1449
JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
IS - 11
ER -