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      • University of Waikato Theses
      • Masters Degree Theses
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      Regression modelling of spectroscopic data using lazy learning and deep neural networks

      Fraser, Huon
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      https://hdl.handle.net/10289/15003
      Abstract
      Neural 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.
      Date
      2022
      Type
      Thesis
      Degree Name
      Master of Science (Research) (MSc(Research))
      Supervisors
      Holmes, Geoffrey
      Fletcher, Dale
      Bifet, Albert
      Publisher
      The University of Waikato
      Rights
      All items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
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      • Masters Degree Theses [2316]
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