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dc.contributor.advisorHolmes, Geoffrey
dc.contributor.advisorFletcher, Dale
dc.contributor.advisorBifet, Albert
dc.contributor.authorFraser, Huon
dc.date.accessioned2022-08-03T21:32:41Z
dc.date.available2022-08-03T21:32:41Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/10289/15003
dc.description.abstractNeural networks show promise in modelling infrared (IR) spectroscopic data, but many of the proposed solutions in the literature are either Multilayer Perceptrons with a single hidden layer or are adapted from successful solutions in other domains such as convolutional networks from image processing. This thesis investigates the use of Monte Carlo search to build candidate deep neural networks from scratch. As the prediction targets are real numbers and the modelling is prone to error from outliers, lazy, locally weighted methods are also investigated. Predictions from the network are compared to predictions made from locally weighting the features extracted from the same network. To measure progress, these results are compared to classical statistical methods commonly used to model spectroscopic data, such as partial least squares (PLS) regression. Results suggest that deep networks trained via Monte-Carlo search outperform classical approaches. They also show that PLS dimension reduction as a preprocessing technique can improve the quality of search at little cost in predictive ability. Encouraging results using this approach are obtained on the publicly available Mangoes dataset. Finally, a streaming regression solution is investigated in which selected deep neural network models perform well. In this setting, however, locally weighted regression can result in sporadic outlier predictions, so an ensemble solution is proposed as a remedy.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherThe University of Waikato
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectRegression
dc.subjectMachine learning
dc.subjectDeep learning
dc.subjectNeural networks
dc.subjectSpectroscopic data
dc.subjectLazy learning
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshMachine learning -- Data processing
dc.subject.lcshMonte Carlo method -- Data processing
dc.subject.lcshDeep learning (Machine learning)
dc.subject.lcshInfrared spectroscopy -- Data processing
dc.subject.lcshRegression analysis -- Data processing
dc.titleRegression modelling of spectroscopic data using lazy learning and deep neural networks
dc.typeThesis
thesis.degree.grantorThe University of Waikato
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (Research) (MSc(Research))
dc.date.updated2022-07-21T03:35:42Z
pubs.place-of-publicationHamilton, New Zealanden_NZ


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