Subagging for the improvement of predictive stability of extreme learning machine for spectral quantitative analysis of complex samples
Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Caixia [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
6 |
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| Übergeordnetes Werk: |
Enthalten in: Migration and characterisation of nanosilver from food containers by AF4-ICP-MS - Artiaga, G. ELSEVIER, 2015, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:161 ; year:2017 ; day:15 ; month:02 ; pages:43-48 ; extent:6 |
| Links: |
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| DOI / URN: |
10.1016/j.chemolab.2016.10.019 |
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| Katalog-ID: |
ELV040342387 |
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| 520 | |a Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. | ||
| 520 | |a Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. | ||
| 650 | 7 | |a Extreme learning machine |2 Elsevier | |
| 650 | 7 | |a Complex samples |2 Elsevier | |
| 650 | 7 | |a Multivariate calibration |2 Elsevier | |
| 650 | 7 | |a Spectral analysis |2 Elsevier | |
| 650 | 7 | |a Ensemble modeling |2 Elsevier | |
| 700 | 1 | |a Bian, Xihui |4 oth | |
| 700 | 1 | |a Liu, Peng |4 oth | |
| 700 | 1 | |a Tan, Xiaoyao |4 oth | |
| 700 | 1 | |a Fan, Qingjie |4 oth | |
| 700 | 1 | |a Liu, Wei |4 oth | |
| 700 | 1 | |a Lin, Ligang |4 oth | |
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10.1016/j.chemolab.2016.10.019 doi GBVA2017006000024.pica (DE-627)ELV040342387 (ELSEVIER)S0169-7439(16)30229-5 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Zhang, Caixia verfasserin aut Subagging for the improvement of predictive stability of extreme learning machine for spectral quantitative analysis of complex samples 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. Extreme learning machine Elsevier Complex samples Elsevier Multivariate calibration Elsevier Spectral analysis Elsevier Ensemble modeling Elsevier Bian, Xihui oth Liu, Peng oth Tan, Xiaoyao oth Fan, Qingjie oth Liu, Wei oth Lin, Ligang oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:161 year:2017 day:15 month:02 pages:43-48 extent:6 https://doi.org/10.1016/j.chemolab.2016.10.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 161 2017 15 0215 43-48 6 045F 540 |
| spelling |
10.1016/j.chemolab.2016.10.019 doi GBVA2017006000024.pica (DE-627)ELV040342387 (ELSEVIER)S0169-7439(16)30229-5 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Zhang, Caixia verfasserin aut Subagging for the improvement of predictive stability of extreme learning machine for spectral quantitative analysis of complex samples 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. Extreme learning machine Elsevier Complex samples Elsevier Multivariate calibration Elsevier Spectral analysis Elsevier Ensemble modeling Elsevier Bian, Xihui oth Liu, Peng oth Tan, Xiaoyao oth Fan, Qingjie oth Liu, Wei oth Lin, Ligang oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:161 year:2017 day:15 month:02 pages:43-48 extent:6 https://doi.org/10.1016/j.chemolab.2016.10.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 161 2017 15 0215 43-48 6 045F 540 |
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10.1016/j.chemolab.2016.10.019 doi GBVA2017006000024.pica (DE-627)ELV040342387 (ELSEVIER)S0169-7439(16)30229-5 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Zhang, Caixia verfasserin aut Subagging for the improvement of predictive stability of extreme learning machine for spectral quantitative analysis of complex samples 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. Extreme learning machine Elsevier Complex samples Elsevier Multivariate calibration Elsevier Spectral analysis Elsevier Ensemble modeling Elsevier Bian, Xihui oth Liu, Peng oth Tan, Xiaoyao oth Fan, Qingjie oth Liu, Wei oth Lin, Ligang oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:161 year:2017 day:15 month:02 pages:43-48 extent:6 https://doi.org/10.1016/j.chemolab.2016.10.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 161 2017 15 0215 43-48 6 045F 540 |
| allfieldsGer |
10.1016/j.chemolab.2016.10.019 doi GBVA2017006000024.pica (DE-627)ELV040342387 (ELSEVIER)S0169-7439(16)30229-5 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Zhang, Caixia verfasserin aut Subagging for the improvement of predictive stability of extreme learning machine for spectral quantitative analysis of complex samples 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. Extreme learning machine Elsevier Complex samples Elsevier Multivariate calibration Elsevier Spectral analysis Elsevier Ensemble modeling Elsevier Bian, Xihui oth Liu, Peng oth Tan, Xiaoyao oth Fan, Qingjie oth Liu, Wei oth Lin, Ligang oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:161 year:2017 day:15 month:02 pages:43-48 extent:6 https://doi.org/10.1016/j.chemolab.2016.10.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 161 2017 15 0215 43-48 6 045F 540 |
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10.1016/j.chemolab.2016.10.019 doi GBVA2017006000024.pica (DE-627)ELV040342387 (ELSEVIER)S0169-7439(16)30229-5 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Zhang, Caixia verfasserin aut Subagging for the improvement of predictive stability of extreme learning machine for spectral quantitative analysis of complex samples 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. Extreme learning machine Elsevier Complex samples Elsevier Multivariate calibration Elsevier Spectral analysis Elsevier Ensemble modeling Elsevier Bian, Xihui oth Liu, Peng oth Tan, Xiaoyao oth Fan, Qingjie oth Liu, Wei oth Lin, Ligang oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:161 year:2017 day:15 month:02 pages:43-48 extent:6 https://doi.org/10.1016/j.chemolab.2016.10.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 161 2017 15 0215 43-48 6 045F 540 |
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Enthalten in Migration and characterisation of nanosilver from food containers by AF4-ICP-MS Amsterdam [u.a.] volume:161 year:2017 day:15 month:02 pages:43-48 extent:6 |
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Enthalten in Migration and characterisation of nanosilver from food containers by AF4-ICP-MS Amsterdam [u.a.] volume:161 year:2017 day:15 month:02 pages:43-48 extent:6 |
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subagging for the improvement of predictive stability of extreme learning machine for spectral quantitative analysis of complex samples |
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Subagging for the improvement of predictive stability of extreme learning machine for spectral quantitative analysis of complex samples |
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Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. |
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Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. |
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Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM. |
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Bian, Xihui Liu, Peng Tan, Xiaoyao Fan, Qingjie Liu, Wei Lin, Ligang |
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