Authors’ Reply to the Comments by Kamata et al.

Bo Zhou, Benhui Chen, Jinglu Hu

Research output: Contribution to journalArticlepeer-review

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].

Original languageEnglish
Pages (from-to)1446-1449
Number of pages4
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE106.A
Issue number11
DOIs
Publication statusPublished - 2023 Nov

Keywords

  • classification
  • machine learning
  • quasi-linear kernel function
  • support vector machine
  • system modeling and parameter estimation

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

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