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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
En 2014, l'article ci-dessus intitulé « Machine à vecteurs de support quasi-linéaire pour la classification non linéaire » a été publié par Zhou et al. [1]. Ils ont proposé une fonction de noyau quasi-linéaire pour machine à vecteurs de support (SVM). Cependant, dans cette lettre, nous soulignons que cette fonction de noyau proposée fait partie des fonctions de noyau multiples générées par l'apprentissage à noyau multiple bien connu proposé par Bach et al. [2] en 2004. Depuis lors, il y a eu de nombreux articles sur l'apprentissage multi-noyau avec plusieurs applications [3]. Cette lettre vérifie que la fonction principale du noyau proposée par Zhou et al. [1] peut être dérivé en utilisant plusieurs algorithmes d'apprentissage du noyau [3]. Dans la construction du noyau, Zhou et al. [1] utilisaient des noyaux gaussiens, mais l'apprentissage à noyaux multiples avait déjà discuté de la localité des noyaux gaussiens additifs ou d'autres noyaux dans le cadre [4], [5]. Les noyaux gaussiens ou autres additifs en particulier ont été discutés dans un didacticiel lors de la grande conférence internationale ECCV2012 [6]. Les auteurs n'ont pas discuté de ces questions.
Sei-ichiro KAMATA
Waseda University
Tsunenori MINE
Kyushu University
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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},}
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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 -