Least squares support vector regression and interval type-2 fuzzy density weight for scene denoising
Abstract Support vector machines are the popular machine learning techniques. Its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, the fitting of the samples contaminated by noises in the training phase will result in the fact that LS-SVR cannot wor...
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| Autor*in: |
Xu, Shuqiong [verfasserIn] Liu, Zhi [verfasserIn] Zhang, Yun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 20(2015), 4 vom: 29. Jan., Seite 1459-1470 |
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| Übergeordnetes Werk: |
volume:20 ; year:2015 ; number:4 ; day:29 ; month:01 ; pages:1459-1470 |
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| DOI / URN: |
10.1007/s00500-015-1598-4 |
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| 520 | |a Abstract Support vector machines are the popular machine learning techniques. Its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, the fitting of the samples contaminated by noises in the training phase will result in the fact that LS-SVR cannot work well when noise level is too far from it or noise density is high. Type-2 fuzzy sets and systems have been shown to be a more promising method to manifest the uncertainties. Various noises would be taken as uncertainties in scene images. By integrating the design of learning weights with type-2 fuzzy sets, a systematic design methodology of interval type-2 fuzzy density weighted support vector regression (IT2FDW-SVR) model for scene denoising is presented to address the problem of sample uncertainty in scene images. A novel strategy is used to design the learning weights, which is similar to the selection of human experience. To handle the uncertainty of sample density, interval type-2 fuzzy logic system (IT2FLS) is employed to deduce the fuzzy learning weights (IT2FDW) in the IT2FDW-SVR, which is an extension of the previously weighted SVR. Extensive experimental results demonstrate that the proposed method can achieve better performances in terms of both objective and subjective evaluations than those state-of-the-art denoising techniques. | ||
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10.1007/s00500-015-1598-4 doi (DE-627)SPR006489621 (SPR)s00500-015-1598-4-e DE-627 ger DE-627 rakwb eng Xu, Shuqiong verfasserin aut Least squares support vector regression and interval type-2 fuzzy density weight for scene denoising 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Support vector machines are the popular machine learning techniques. Its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, the fitting of the samples contaminated by noises in the training phase will result in the fact that LS-SVR cannot work well when noise level is too far from it or noise density is high. Type-2 fuzzy sets and systems have been shown to be a more promising method to manifest the uncertainties. Various noises would be taken as uncertainties in scene images. By integrating the design of learning weights with type-2 fuzzy sets, a systematic design methodology of interval type-2 fuzzy density weighted support vector regression (IT2FDW-SVR) model for scene denoising is presented to address the problem of sample uncertainty in scene images. A novel strategy is used to design the learning weights, which is similar to the selection of human experience. To handle the uncertainty of sample density, interval type-2 fuzzy logic system (IT2FLS) is employed to deduce the fuzzy learning weights (IT2FDW) in the IT2FDW-SVR, which is an extension of the previously weighted SVR. Extensive experimental results demonstrate that the proposed method can achieve better performances in terms of both objective and subjective evaluations than those state-of-the-art denoising techniques. Interval type-2 fuzzy density weights (dpeaa)DE-He213 Fuzzy logic system (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Liu, Zhi verfasserin aut Zhang, Yun verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2015), 4 vom: 29. Jan., Seite 1459-1470 (DE-627)SPR006469531 nnns volume:20 year:2015 number:4 day:29 month:01 pages:1459-1470 https://dx.doi.org/10.1007/s00500-015-1598-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2015 4 29 01 1459-1470 |
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10.1007/s00500-015-1598-4 doi (DE-627)SPR006489621 (SPR)s00500-015-1598-4-e DE-627 ger DE-627 rakwb eng Xu, Shuqiong verfasserin aut Least squares support vector regression and interval type-2 fuzzy density weight for scene denoising 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Support vector machines are the popular machine learning techniques. Its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, the fitting of the samples contaminated by noises in the training phase will result in the fact that LS-SVR cannot work well when noise level is too far from it or noise density is high. Type-2 fuzzy sets and systems have been shown to be a more promising method to manifest the uncertainties. Various noises would be taken as uncertainties in scene images. By integrating the design of learning weights with type-2 fuzzy sets, a systematic design methodology of interval type-2 fuzzy density weighted support vector regression (IT2FDW-SVR) model for scene denoising is presented to address the problem of sample uncertainty in scene images. A novel strategy is used to design the learning weights, which is similar to the selection of human experience. To handle the uncertainty of sample density, interval type-2 fuzzy logic system (IT2FLS) is employed to deduce the fuzzy learning weights (IT2FDW) in the IT2FDW-SVR, which is an extension of the previously weighted SVR. Extensive experimental results demonstrate that the proposed method can achieve better performances in terms of both objective and subjective evaluations than those state-of-the-art denoising techniques. Interval type-2 fuzzy density weights (dpeaa)DE-He213 Fuzzy logic system (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Liu, Zhi verfasserin aut Zhang, Yun verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2015), 4 vom: 29. Jan., Seite 1459-1470 (DE-627)SPR006469531 nnns volume:20 year:2015 number:4 day:29 month:01 pages:1459-1470 https://dx.doi.org/10.1007/s00500-015-1598-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2015 4 29 01 1459-1470 |
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10.1007/s00500-015-1598-4 doi (DE-627)SPR006489621 (SPR)s00500-015-1598-4-e DE-627 ger DE-627 rakwb eng Xu, Shuqiong verfasserin aut Least squares support vector regression and interval type-2 fuzzy density weight for scene denoising 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Support vector machines are the popular machine learning techniques. Its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, the fitting of the samples contaminated by noises in the training phase will result in the fact that LS-SVR cannot work well when noise level is too far from it or noise density is high. Type-2 fuzzy sets and systems have been shown to be a more promising method to manifest the uncertainties. Various noises would be taken as uncertainties in scene images. By integrating the design of learning weights with type-2 fuzzy sets, a systematic design methodology of interval type-2 fuzzy density weighted support vector regression (IT2FDW-SVR) model for scene denoising is presented to address the problem of sample uncertainty in scene images. A novel strategy is used to design the learning weights, which is similar to the selection of human experience. To handle the uncertainty of sample density, interval type-2 fuzzy logic system (IT2FLS) is employed to deduce the fuzzy learning weights (IT2FDW) in the IT2FDW-SVR, which is an extension of the previously weighted SVR. Extensive experimental results demonstrate that the proposed method can achieve better performances in terms of both objective and subjective evaluations than those state-of-the-art denoising techniques. Interval type-2 fuzzy density weights (dpeaa)DE-He213 Fuzzy logic system (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Liu, Zhi verfasserin aut Zhang, Yun verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2015), 4 vom: 29. Jan., Seite 1459-1470 (DE-627)SPR006469531 nnns volume:20 year:2015 number:4 day:29 month:01 pages:1459-1470 https://dx.doi.org/10.1007/s00500-015-1598-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2015 4 29 01 1459-1470 |
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10.1007/s00500-015-1598-4 doi (DE-627)SPR006489621 (SPR)s00500-015-1598-4-e DE-627 ger DE-627 rakwb eng Xu, Shuqiong verfasserin aut Least squares support vector regression and interval type-2 fuzzy density weight for scene denoising 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Support vector machines are the popular machine learning techniques. Its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, the fitting of the samples contaminated by noises in the training phase will result in the fact that LS-SVR cannot work well when noise level is too far from it or noise density is high. Type-2 fuzzy sets and systems have been shown to be a more promising method to manifest the uncertainties. Various noises would be taken as uncertainties in scene images. By integrating the design of learning weights with type-2 fuzzy sets, a systematic design methodology of interval type-2 fuzzy density weighted support vector regression (IT2FDW-SVR) model for scene denoising is presented to address the problem of sample uncertainty in scene images. A novel strategy is used to design the learning weights, which is similar to the selection of human experience. To handle the uncertainty of sample density, interval type-2 fuzzy logic system (IT2FLS) is employed to deduce the fuzzy learning weights (IT2FDW) in the IT2FDW-SVR, which is an extension of the previously weighted SVR. Extensive experimental results demonstrate that the proposed method can achieve better performances in terms of both objective and subjective evaluations than those state-of-the-art denoising techniques. Interval type-2 fuzzy density weights (dpeaa)DE-He213 Fuzzy logic system (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Liu, Zhi verfasserin aut Zhang, Yun verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2015), 4 vom: 29. Jan., Seite 1459-1470 (DE-627)SPR006469531 nnns volume:20 year:2015 number:4 day:29 month:01 pages:1459-1470 https://dx.doi.org/10.1007/s00500-015-1598-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2015 4 29 01 1459-1470 |
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Abstract Support vector machines are the popular machine learning techniques. Its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, the fitting of the samples contaminated by noises in the training phase will result in the fact that LS-SVR cannot work well when noise level is too far from it or noise density is high. Type-2 fuzzy sets and systems have been shown to be a more promising method to manifest the uncertainties. Various noises would be taken as uncertainties in scene images. By integrating the design of learning weights with type-2 fuzzy sets, a systematic design methodology of interval type-2 fuzzy density weighted support vector regression (IT2FDW-SVR) model for scene denoising is presented to address the problem of sample uncertainty in scene images. A novel strategy is used to design the learning weights, which is similar to the selection of human experience. To handle the uncertainty of sample density, interval type-2 fuzzy logic system (IT2FLS) is employed to deduce the fuzzy learning weights (IT2FDW) in the IT2FDW-SVR, which is an extension of the previously weighted SVR. Extensive experimental results demonstrate that the proposed method can achieve better performances in terms of both objective and subjective evaluations than those state-of-the-art denoising techniques. |
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Abstract Support vector machines are the popular machine learning techniques. Its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, the fitting of the samples contaminated by noises in the training phase will result in the fact that LS-SVR cannot work well when noise level is too far from it or noise density is high. Type-2 fuzzy sets and systems have been shown to be a more promising method to manifest the uncertainties. Various noises would be taken as uncertainties in scene images. By integrating the design of learning weights with type-2 fuzzy sets, a systematic design methodology of interval type-2 fuzzy density weighted support vector regression (IT2FDW-SVR) model for scene denoising is presented to address the problem of sample uncertainty in scene images. A novel strategy is used to design the learning weights, which is similar to the selection of human experience. To handle the uncertainty of sample density, interval type-2 fuzzy logic system (IT2FLS) is employed to deduce the fuzzy learning weights (IT2FDW) in the IT2FDW-SVR, which is an extension of the previously weighted SVR. Extensive experimental results demonstrate that the proposed method can achieve better performances in terms of both objective and subjective evaluations than those state-of-the-art denoising techniques. |
| abstract_unstemmed |
Abstract Support vector machines are the popular machine learning techniques. Its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, the fitting of the samples contaminated by noises in the training phase will result in the fact that LS-SVR cannot work well when noise level is too far from it or noise density is high. Type-2 fuzzy sets and systems have been shown to be a more promising method to manifest the uncertainties. Various noises would be taken as uncertainties in scene images. By integrating the design of learning weights with type-2 fuzzy sets, a systematic design methodology of interval type-2 fuzzy density weighted support vector regression (IT2FDW-SVR) model for scene denoising is presented to address the problem of sample uncertainty in scene images. A novel strategy is used to design the learning weights, which is similar to the selection of human experience. To handle the uncertainty of sample density, interval type-2 fuzzy logic system (IT2FLS) is employed to deduce the fuzzy learning weights (IT2FDW) in the IT2FDW-SVR, which is an extension of the previously weighted SVR. Extensive experimental results demonstrate that the proposed method can achieve better performances in terms of both objective and subjective evaluations than those state-of-the-art denoising techniques. |
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Least squares support vector regression and interval type-2 fuzzy density weight for scene denoising |
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https://dx.doi.org/10.1007/s00500-015-1598-4 |
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Liu, Zhi Zhang, Yun |
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10.1007/s00500-015-1598-4 |
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