The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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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
Pada tahun 2014, kertas kerja di atas bertajuk 'Mesin Vektor Sokongan Kuasi-Linear untuk Pengelasan Tak Linear' telah diterbitkan oleh Zhou, et al. [1]. Mereka mencadangkan fungsi inti kuasi-linear untuk mesin vektor sokongan (SVM). Walau bagaimanapun, dalam surat ini, kami menunjukkan bahawa fungsi kernel yang dicadangkan ini adalah sebahagian daripada fungsi kernel berbilang yang dijana oleh pembelajaran kernel berbilang terkenal yang dicadangkan oleh Bach, et al. [2] pada tahun 2004. Sejak itu, terdapat banyak kertas kerja berkaitan tentang pembelajaran kernel berbilang dengan beberapa aplikasi [3]. Surat ini mengesahkan bahawa fungsi inti utama yang dicadangkan oleh Zhou, et al. [1] boleh diperolehi menggunakan pelbagai algoritma pembelajaran kernel [3]. Dalam pembinaan kernel, Zhou, et al. [1] menggunakan kernel Gaussian, tetapi pembelajaran kernel berbilang telah membincangkan lokaliti kernel Gaussian aditif atau kernel lain dalam rangka kerja [4], [5]. Terutamanya aditif Gaussian atau kernel lain telah dibincangkan dalam tutorial di persidangan antarabangsa utama ECCV2012 [6]. Penulis tidak membincangkan perkara ini.
Sei-ichiro KAMATA
Waseda University
Tsunenori MINE
Kyushu University
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Salinan
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
Salinan
@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},}
Salinan
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 -