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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| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
2010
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| 在線閱讀: | http://hdl.handle.net/10356/39416 |
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| 總結: | 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. |
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