New learning rules for the ASSOM network
Abstract The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Le...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
López-Rubio, Ezequiel [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2003 |
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| Schlagwörter: |
Adaptive subspace self-organising map (ASSOM) Principal Component Analysis (PCA) |
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| Anmerkung: |
© Springer-Verlag London 2003 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer-Verlag, 1993, 12(2003), 2 vom: Nov., Seite 109-118 |
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| Übergeordnetes Werk: |
volume:12 ; year:2003 ; number:2 ; month:11 ; pages:109-118 |
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| DOI / URN: |
10.1007/s00521-003-0376-x |
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OLC2025578717 |
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| 520 | |a Abstract The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Levenberg–Marquardt method. Each of these two approaches gives a different learning rule. A comparison is carried out among the original Kohonen’s method and the proposed learning rules. Experimental results are reported, including a convergence speed experiment and a speech processing application, which show that the new learning rules have better performance than the original one. | ||
| 650 | 4 | |a Adaptive subspace self-organising map (ASSOM) | |
| 650 | 4 | |a Competitive learning | |
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| 700 | 1 | |a Muñoz-Pérez, José |4 aut | |
| 700 | 1 | |a Gómez-Ruiz, José Antonio |4 aut | |
| 700 | 1 | |a Domínguez-Merino, Enrique |4 aut | |
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10.1007/s00521-003-0376-x doi (DE-627)OLC2025578717 (DE-He213)s00521-003-0376-x-p DE-627 ger DE-627 rakwb eng 004 VZ López-Rubio, Ezequiel verfasserin aut New learning rules for the ASSOM network 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2003 Abstract The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Levenberg–Marquardt method. Each of these two approaches gives a different learning rule. A comparison is carried out among the original Kohonen’s method and the proposed learning rules. Experimental results are reported, including a convergence speed experiment and a speech processing application, which show that the new learning rules have better performance than the original one. Adaptive subspace self-organising map (ASSOM) Competitive learning Principal Component Analysis (PCA) Self-organisation Self-organising feature map (SOFM) Unsupervised learning Muñoz-Pérez, José aut Gómez-Ruiz, José Antonio aut Domínguez-Merino, Enrique aut Enthalten in Neural computing & applications Springer-Verlag, 1993 12(2003), 2 vom: Nov., Seite 109-118 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:12 year:2003 number:2 month:11 pages:109-118 https://doi.org/10.1007/s00521-003-0376-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2190 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4334 AR 12 2003 2 11 109-118 |
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10.1007/s00521-003-0376-x doi (DE-627)OLC2025578717 (DE-He213)s00521-003-0376-x-p DE-627 ger DE-627 rakwb eng 004 VZ López-Rubio, Ezequiel verfasserin aut New learning rules for the ASSOM network 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2003 Abstract The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Levenberg–Marquardt method. Each of these two approaches gives a different learning rule. A comparison is carried out among the original Kohonen’s method and the proposed learning rules. Experimental results are reported, including a convergence speed experiment and a speech processing application, which show that the new learning rules have better performance than the original one. Adaptive subspace self-organising map (ASSOM) Competitive learning Principal Component Analysis (PCA) Self-organisation Self-organising feature map (SOFM) Unsupervised learning Muñoz-Pérez, José aut Gómez-Ruiz, José Antonio aut Domínguez-Merino, Enrique aut Enthalten in Neural computing & applications Springer-Verlag, 1993 12(2003), 2 vom: Nov., Seite 109-118 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:12 year:2003 number:2 month:11 pages:109-118 https://doi.org/10.1007/s00521-003-0376-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2190 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4334 AR 12 2003 2 11 109-118 |
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10.1007/s00521-003-0376-x doi (DE-627)OLC2025578717 (DE-He213)s00521-003-0376-x-p DE-627 ger DE-627 rakwb eng 004 VZ López-Rubio, Ezequiel verfasserin aut New learning rules for the ASSOM network 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2003 Abstract The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Levenberg–Marquardt method. Each of these two approaches gives a different learning rule. A comparison is carried out among the original Kohonen’s method and the proposed learning rules. Experimental results are reported, including a convergence speed experiment and a speech processing application, which show that the new learning rules have better performance than the original one. Adaptive subspace self-organising map (ASSOM) Competitive learning Principal Component Analysis (PCA) Self-organisation Self-organising feature map (SOFM) Unsupervised learning Muñoz-Pérez, José aut Gómez-Ruiz, José Antonio aut Domínguez-Merino, Enrique aut Enthalten in Neural computing & applications Springer-Verlag, 1993 12(2003), 2 vom: Nov., Seite 109-118 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:12 year:2003 number:2 month:11 pages:109-118 https://doi.org/10.1007/s00521-003-0376-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2190 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4334 AR 12 2003 2 11 109-118 |
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10.1007/s00521-003-0376-x doi (DE-627)OLC2025578717 (DE-He213)s00521-003-0376-x-p DE-627 ger DE-627 rakwb eng 004 VZ López-Rubio, Ezequiel verfasserin aut New learning rules for the ASSOM network 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2003 Abstract The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Levenberg–Marquardt method. Each of these two approaches gives a different learning rule. A comparison is carried out among the original Kohonen’s method and the proposed learning rules. Experimental results are reported, including a convergence speed experiment and a speech processing application, which show that the new learning rules have better performance than the original one. Adaptive subspace self-organising map (ASSOM) Competitive learning Principal Component Analysis (PCA) Self-organisation Self-organising feature map (SOFM) Unsupervised learning Muñoz-Pérez, José aut Gómez-Ruiz, José Antonio aut Domínguez-Merino, Enrique aut Enthalten in Neural computing & applications Springer-Verlag, 1993 12(2003), 2 vom: Nov., Seite 109-118 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:12 year:2003 number:2 month:11 pages:109-118 https://doi.org/10.1007/s00521-003-0376-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2190 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4334 AR 12 2003 2 11 109-118 |
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10.1007/s00521-003-0376-x doi (DE-627)OLC2025578717 (DE-He213)s00521-003-0376-x-p DE-627 ger DE-627 rakwb eng 004 VZ López-Rubio, Ezequiel verfasserin aut New learning rules for the ASSOM network 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2003 Abstract The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Levenberg–Marquardt method. Each of these two approaches gives a different learning rule. A comparison is carried out among the original Kohonen’s method and the proposed learning rules. Experimental results are reported, including a convergence speed experiment and a speech processing application, which show that the new learning rules have better performance than the original one. Adaptive subspace self-organising map (ASSOM) Competitive learning Principal Component Analysis (PCA) Self-organisation Self-organising feature map (SOFM) Unsupervised learning Muñoz-Pérez, José aut Gómez-Ruiz, José Antonio aut Domínguez-Merino, Enrique aut Enthalten in Neural computing & applications Springer-Verlag, 1993 12(2003), 2 vom: Nov., Seite 109-118 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:12 year:2003 number:2 month:11 pages:109-118 https://doi.org/10.1007/s00521-003-0376-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2190 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4334 AR 12 2003 2 11 109-118 |
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Abstract The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Levenberg–Marquardt method. Each of these two approaches gives a different learning rule. A comparison is carried out among the original Kohonen’s method and the proposed learning rules. Experimental results are reported, including a convergence speed experiment and a speech processing application, which show that the new learning rules have better performance than the original one. © Springer-Verlag London 2003 |
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Abstract The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Levenberg–Marquardt method. Each of these two approaches gives a different learning rule. A comparison is carried out among the original Kohonen’s method and the proposed learning rules. Experimental results are reported, including a convergence speed experiment and a speech processing application, which show that the new learning rules have better performance than the original one. © Springer-Verlag London 2003 |
| abstract_unstemmed |
Abstract The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Levenberg–Marquardt method. Each of these two approaches gives a different learning rule. A comparison is carried out among the original Kohonen’s method and the proposed learning rules. Experimental results are reported, including a convergence speed experiment and a speech processing application, which show that the new learning rules have better performance than the original one. © Springer-Verlag London 2003 |
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New learning rules for the ASSOM network |
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Muñoz-Pérez, José Gómez-Ruiz, José Antonio Domínguez-Merino, Enrique |
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Muñoz-Pérez, José Gómez-Ruiz, José Antonio Domínguez-Merino, Enrique |
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2024-07-04T01:35:59.878Z |
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