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...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Tan, Jonathan Jun Wei.
مؤلفون آخرون: School of Chemical and Biomedical Engineering
التنسيق: Final Year Project
اللغة:English
منشور في: 2010
الموضوعات:
الوصول للمادة أونلاين:http://hdl.handle.net/10356/39416
الوسوم: إضافة وسم
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الوصف
الملخص: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.