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 language | English |
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Pages (from-to) | 1446-1449 |
Number of pages | 4 |
Journal | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |
Volume | E106.A |
Issue number | 11 |
DOIs | |
Publication status | Published - 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