A Fast Dual Algorithm for Kernel Logistic Regression
Abstract This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computationa...
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| Autor*in: |
Keerthi, S. S. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2005 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science + Business Media, Inc. 2005 |
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| Übergeordnetes Werk: |
Enthalten in: Machine learning - Kluwer Academic Publishers, 1986, 61(2005), 1-3 vom: 11. Juli, Seite 151-165 |
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| Übergeordnetes Werk: |
volume:61 ; year:2005 ; number:1-3 ; day:11 ; month:07 ; pages:151-165 |
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10.1007/s10994-005-0768-5 |
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| 520 | |a Abstract This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers. | ||
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10.1007/s10994-005-0768-5 doi (DE-627)OLC2026520615 (DE-He213)s10994-005-0768-5-p DE-627 ger DE-627 rakwb eng 150 004 VZ Keerthi, S. S. verfasserin aut A Fast Dual Algorithm for Kernel Logistic Regression 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science + Business Media, Inc. 2005 Abstract This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers. classification logistic regression kernel methods SMO algorithm Duan, K. B. aut Shevade, S. K. aut Poo, A. N. aut Enthalten in Machine learning Kluwer Academic Publishers, 1986 61(2005), 1-3 vom: 11. Juli, Seite 151-165 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:61 year:2005 number:1-3 day:11 month:07 pages:151-165 https://doi.org/10.1007/s10994-005-0768-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_70 GBV_ILN_100 GBV_ILN_130 GBV_ILN_152 GBV_ILN_2006 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 61 2005 1-3 11 07 151-165 |
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10.1007/s10994-005-0768-5 doi (DE-627)OLC2026520615 (DE-He213)s10994-005-0768-5-p DE-627 ger DE-627 rakwb eng 150 004 VZ Keerthi, S. S. verfasserin aut A Fast Dual Algorithm for Kernel Logistic Regression 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science + Business Media, Inc. 2005 Abstract This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers. classification logistic regression kernel methods SMO algorithm Duan, K. B. aut Shevade, S. K. aut Poo, A. N. aut Enthalten in Machine learning Kluwer Academic Publishers, 1986 61(2005), 1-3 vom: 11. Juli, Seite 151-165 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:61 year:2005 number:1-3 day:11 month:07 pages:151-165 https://doi.org/10.1007/s10994-005-0768-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_70 GBV_ILN_100 GBV_ILN_130 GBV_ILN_152 GBV_ILN_2006 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 61 2005 1-3 11 07 151-165 |
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10.1007/s10994-005-0768-5 doi (DE-627)OLC2026520615 (DE-He213)s10994-005-0768-5-p DE-627 ger DE-627 rakwb eng 150 004 VZ Keerthi, S. S. verfasserin aut A Fast Dual Algorithm for Kernel Logistic Regression 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science + Business Media, Inc. 2005 Abstract This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers. classification logistic regression kernel methods SMO algorithm Duan, K. B. aut Shevade, S. K. aut Poo, A. N. aut Enthalten in Machine learning Kluwer Academic Publishers, 1986 61(2005), 1-3 vom: 11. Juli, Seite 151-165 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:61 year:2005 number:1-3 day:11 month:07 pages:151-165 https://doi.org/10.1007/s10994-005-0768-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_70 GBV_ILN_100 GBV_ILN_130 GBV_ILN_152 GBV_ILN_2006 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 61 2005 1-3 11 07 151-165 |
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10.1007/s10994-005-0768-5 doi (DE-627)OLC2026520615 (DE-He213)s10994-005-0768-5-p DE-627 ger DE-627 rakwb eng 150 004 VZ Keerthi, S. S. verfasserin aut A Fast Dual Algorithm for Kernel Logistic Regression 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science + Business Media, Inc. 2005 Abstract This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers. classification logistic regression kernel methods SMO algorithm Duan, K. B. aut Shevade, S. K. aut Poo, A. N. aut Enthalten in Machine learning Kluwer Academic Publishers, 1986 61(2005), 1-3 vom: 11. Juli, Seite 151-165 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:61 year:2005 number:1-3 day:11 month:07 pages:151-165 https://doi.org/10.1007/s10994-005-0768-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_70 GBV_ILN_100 GBV_ILN_130 GBV_ILN_152 GBV_ILN_2006 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 61 2005 1-3 11 07 151-165 |
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Abstract This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers. © Springer Science + Business Media, Inc. 2005 |
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Abstract This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers. © Springer Science + Business Media, Inc. 2005 |
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Abstract This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers. © Springer Science + Business Media, Inc. 2005 |
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A Fast Dual Algorithm for Kernel Logistic Regression |
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Duan, K. B. Shevade, S. K. Poo, A. N. |
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