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Theses and Dissertations
This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.
Items in this Collection
- 3Associative Classification
- 1Expected Support
- 1Explainable AI
- 1Machine Learning
- 1Model Independent Explanation
- 1Sequential Patterns
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Fall 2011
Several research projects explore the application of uncertain databases which contain probabilistic attributes. Uncertainty in data can be caused by inherent randomness, imprecision in measuring equipment, ambiguity, information extraction from unstructured data, etc. The classification and...
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Statistically Significant Dependencies for Spatial Co-location Pattern Mining and Classification Association Rule Discovery
DownloadFall 2014
Spatial co-location pattern mining and classification association rule discovery are two canonical tasks studied in the data mining community. Both of them focus on the detection of sets of features that show associations. The difference is that in spatial co-location pattern mining, the features...
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Fall 2023
Giving reasons for justifying the decisions made by classification models has received less attention in recent artificial intelligence breakthroughs than improving the accuracy of the models. Recently, AI researchers are paying more attention to filling this gap, leading to the introduction of...