IEICE TRANSACTIONS on Fundamentals
Comments on Quasi-Linear Support Vector Machine for Nonlinear Classification
Summary :
In 2014, the above paper entitled ‘Quasi-Linear Support Vector Machine for Nonlinear Classification’ was published by Zhou, et al. [1]. They proposed a quasi-linear kernel function for support vector machine (SVM). However, in this letter, we point out that this proposed kernel function is a part of multiple kernel functions generated by well-known multiple kernel learning which is proposed by Bach, et al. [2] in 2004. Since then, there have been a lot of related papers on multiple kernel learning with several applications [3]. This letter verifies that the main kernel function proposed by Zhou, et al. [1] can be derived using multiple kernel learning algorithms [3]. In the kernel construction, Zhou, et al. [1] used Gaussian kernels, but the multiple kernel learning had already discussed the locality of additive Gaussian kernels or other kernels in the framework [4], [5]. Especially additive Gaussian or other kernels were discussed in tutorial at major international conference ECCV2012 [6]. The authors did not discuss these matters.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E106-A No.11 pp.1444-1445
- Publication Date
- 2023/11/01
- Publicized
- 2023/05/08
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2022EAL2051
- Type of Manuscript
- WRITTEN DISCUSSION
- Category
- General Fundamentals and Boundaries
Authors
Sei-ichiro KAMATA
Waseda University
Tsunenori MINE
Kyushu University
Keyword
Latest Issue
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Sei-ichiro KAMATA, Tsunenori MINE, "Comments on Quasi-Linear Support Vector Machine for Nonlinear Classification" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 11, pp. 1444-1445, November 2023, doi: 10.1587/transfun.2022EAL2051.
Abstract: In 2014, the above paper entitled ‘Quasi-Linear Support Vector Machine for Nonlinear Classification’ was published by Zhou, et al. [1]. They proposed a quasi-linear kernel function for support vector machine (SVM). However, in this letter, we point out that this proposed kernel function is a part of multiple kernel functions generated by well-known multiple kernel learning which is proposed by Bach, et al. [2] in 2004. Since then, there have been a lot of related papers on multiple kernel learning with several applications [3]. This letter verifies that the main kernel function proposed by Zhou, et al. [1] can be derived using multiple kernel learning algorithms [3]. In the kernel construction, Zhou, et al. [1] used Gaussian kernels, but the multiple kernel learning had already discussed the locality of additive Gaussian kernels or other kernels in the framework [4], [5]. Especially additive Gaussian or other kernels were discussed in tutorial at major international conference ECCV2012 [6]. The authors did not discuss these matters.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022EAL2051/_p
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@ARTICLE{e106-a_11_1444,
author={Sei-ichiro KAMATA, Tsunenori MINE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Comments on Quasi-Linear Support Vector Machine for Nonlinear Classification},
year={2023},
volume={E106-A},
number={11},
pages={1444-1445},
abstract={In 2014, the above paper entitled ‘Quasi-Linear Support Vector Machine for Nonlinear Classification’ was published by Zhou, et al. [1]. They proposed a quasi-linear kernel function for support vector machine (SVM). However, in this letter, we point out that this proposed kernel function is a part of multiple kernel functions generated by well-known multiple kernel learning which is proposed by Bach, et al. [2] in 2004. Since then, there have been a lot of related papers on multiple kernel learning with several applications [3]. This letter verifies that the main kernel function proposed by Zhou, et al. [1] can be derived using multiple kernel learning algorithms [3]. In the kernel construction, Zhou, et al. [1] used Gaussian kernels, but the multiple kernel learning had already discussed the locality of additive Gaussian kernels or other kernels in the framework [4], [5]. Especially additive Gaussian or other kernels were discussed in tutorial at major international conference ECCV2012 [6]. The authors did not discuss these matters.},
keywords={},
doi={10.1587/transfun.2022EAL2051},
ISSN={1745-1337},
month={November},}
Copy
TY - JOUR
TI - Comments on Quasi-Linear Support Vector Machine for Nonlinear Classification
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1444
EP - 1445
AU - Sei-ichiro KAMATA
AU - Tsunenori MINE
PY - 2023
DO - 10.1587/transfun.2022EAL2051
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2023
AB - In 2014, the above paper entitled ‘Quasi-Linear Support Vector Machine for Nonlinear Classification’ was published by Zhou, et al. [1]. They proposed a quasi-linear kernel function for support vector machine (SVM). However, in this letter, we point out that this proposed kernel function is a part of multiple kernel functions generated by well-known multiple kernel learning which is proposed by Bach, et al. [2] in 2004. Since then, there have been a lot of related papers on multiple kernel learning with several applications [3]. This letter verifies that the main kernel function proposed by Zhou, et al. [1] can be derived using multiple kernel learning algorithms [3]. In the kernel construction, Zhou, et al. [1] used Gaussian kernels, but the multiple kernel learning had already discussed the locality of additive Gaussian kernels or other kernels in the framework [4], [5]. Especially additive Gaussian or other kernels were discussed in tutorial at major international conference ECCV2012 [6]. The authors did not discuss these matters.
ER -
English
