IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Online ISSN : 1745-1337
Print ISSN : 0916-8508

This article has now been updated. Please use the final version.

Authors' reply to the comments by Kamata et al
Bo ZHOUBenhui CHENJinglu HU
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JOURNAL FREE ACCESS Advance online publication

Article ID: 2023EAL2006

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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].

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© 2023 The Institute of Electronics, Information and Communication Engineers
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