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Title: Combining multiple models to improve calibration accuracy of spectrometers
Authors: Tan, Jonathan Jun Wei.
Keywords: DRNTU::Science::Chemistry::Biochemistry::Spectroscopy
Issue Date: 2010
Abstract: 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.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Student Reports (FYP/IA/PA/PI)

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