Combining multiple models to improve calibration accuracy of spectrometers
Much recent academic interest had been directed towards various multi-model ensemble techniques to produce more accurate prediction than an individual model. This holds great potential in the field of spectrometric calibration considering the vast usage of spectrometers. In this project, the author...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2010
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| Online Access: | http://hdl.handle.net/10356/39416 |
| _version_ | 1826126405492015104 |
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| author | Tan, Jonathan Jun Wei. |
| author2 | School of Chemical and Biomedical Engineering |
| author_facet | School of Chemical and Biomedical Engineering Tan, Jonathan Jun Wei. |
| author_sort | Tan, Jonathan Jun Wei. |
| collection | NTU |
| description | Much recent academic interest had been directed towards various multi-model ensemble techniques to produce more accurate prediction than an individual model. This holds great potential in the field of spectrometric calibration considering the vast usage of spectrometers. In this project, the author used the bagging and boosting technique as committee machines to complement the Partial Least Squares Regression and Gaussian Process Regression methodologies in the calibration of the "Tablets" and "Meat" dataset. The results indicate their superiority over single model prediction and upon comparing the bagging and boosting algorithm on a single dataset, it appears that the boosting technique is marginally better. |
| first_indexed | 2024-10-01T06:52:07Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/39416 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:52:07Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/394162023-03-03T15:38:20Z Combining multiple models to improve calibration accuracy of spectrometers Tan, Jonathan Jun Wei. School of Chemical and Biomedical Engineering Chen Tao DRNTU::Science::Chemistry::Biochemistry::Spectroscopy Much recent academic interest had been directed towards various multi-model ensemble techniques to produce more accurate prediction than an individual model. This holds great potential in the field of spectrometric calibration considering the vast usage of spectrometers. In this project, the author used the bagging and boosting technique as committee machines to complement the Partial Least Squares Regression and Gaussian Process Regression methodologies in the calibration of the "Tablets" and "Meat" dataset. The results indicate their superiority over single model prediction and upon comparing the bagging and boosting algorithm on a single dataset, it appears that the boosting technique is marginally better. Bachelor of Engineering (Chemical and Biomolecular Engineering) 2010-05-24T02:24:58Z 2010-05-24T02:24:58Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/39416 en Nanyang Technological University 51 p. application/pdf |
| spellingShingle | DRNTU::Science::Chemistry::Biochemistry::Spectroscopy Tan, Jonathan Jun Wei. Combining multiple models to improve calibration accuracy of spectrometers |
| title | Combining multiple models to improve calibration accuracy of spectrometers |
| title_full | Combining multiple models to improve calibration accuracy of spectrometers |
| title_fullStr | Combining multiple models to improve calibration accuracy of spectrometers |
| title_full_unstemmed | Combining multiple models to improve calibration accuracy of spectrometers |
| title_short | Combining multiple models to improve calibration accuracy of spectrometers |
| title_sort | combining multiple models to improve calibration accuracy of spectrometers |
| topic | DRNTU::Science::Chemistry::Biochemistry::Spectroscopy |
| url | http://hdl.handle.net/10356/39416 |
| work_keys_str_mv | AT tanjonathanjunwei combiningmultiplemodelstoimprovecalibrationaccuracyofspectrometers |