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- 144Graduate and Postdoctoral Studies (GPS), Faculty of
- 144Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 8Computing Science, Department of
- 7Computing Science, Department of/Technical Reports (Computing Science)
- 2Chemical and Materials Engineering, Department of
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- 144Thesis
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- 4Article (Published)
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2003
Greiner, Russ, Poulin, B., Lu, Paul, Anvik, J., Lu, Z., Macdonell, Cam, Wishart, David, Eisner, Roman, Szafron, Duane
Technical report TR03-09. Naive Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label. We present a...
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Exploring biomarkers to predict pig disease resilience traits under a natural disease challenge
DownloadSpring 2024
The intensification and consolidation of modern pig production is exposed to higher risks of endemic or pandemic infections. The complexity of the polymicrobial challenge and increasing concerns on antibiotics resistance make it pivotal to find an efficient way of controlling infections besides...
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Fall 2022
Overfitting is a phenomenon when a machine learning system learns the patterns in training data so well that it starts to inauspiciously affect the model performance on unseen data. In practice, machine learning systems that overfit are not deployable rather systems that generalize well and do...
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Spring 2023
As we listen to spoken language, the brain performs multiple levels of computation, from understanding individual words to comprehending the arc of a story. Recently, computational models have been developed that also process text on multiple levels. These models, called multi-timescale long...
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Fall 2016
In this thesis, we investigate the move prediction problem in the game of Go by proposing a new ranking model named Factorization Bradley Terry (FBT) model. This new model considers the move prediction problem as group competitions while also taking the interaction between features into account....
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Fall 2019
Data is becoming more valuable as there are still many uncertainties and hidden information that have yet to be discovered. For this reason, the application of data analysis and machine learning in the industry is becoming more popular. For example, SAGD (steam assisted gravity drainage) is a...
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Fall 2021
Convolutional Neural Networks (CNNs) have been recently seeing great success in various image classification fields and applications. However, this success has been accompanied by a significant increase in memory and computational demands, limiting their use in resource-limited devices, e.g.,...
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Fixed Point Propagation: A New Way To Train Recurrent Neural Networks Using Auxiliary Variables
DownloadFall 2019
Recurrent neural networks (RNNs), along with their many variants, provide a powerful tool for online prediction in partially observable problems. Two issues concerning RNNs, however, are the ability to capture long-term dependencies and long training times. There have been a variety of strategies...
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Fall 2017
This thesis examines the predictability of Canadian recessions with special emphasis on variable selection in a big data environment. The first paper in this thesis addresses the problem of variable selection from a traditional point of view by employing a prescreened set of selected individual...
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Spring 2024
This thesis aims to create a platform to estimate and monitor the University of Alberta (UAlberta) fleet vehicles’ fuel consumption and Carbon Dioxide (CO2) emissions. The main objective is to collect and analyze fleet vehicles information to reduce energy consumption and greenhouse gas emissions...