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- 1Actor-critic methods
- 1Artificial Intelligence
- 1Bias-variance tradeoff
- 1Experimental design
- 1Fixed-depth decision tree
- 1Campbell, Sandy
- 1Farhangfar, Alireza
- 1Fatmi, Mim S.
- 1Graves, Daniel
- 1Hartling, Lisa
- 1Hillier, Tracey
- 3Graduate and Postdoctoral Studies (GPS), Faculty of
- 3Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 1Computing Science, Department of
- 1Computing Science, Department of/Technical Reports (Computing Science)
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- 1Medicine and Dentistry, Faculty of/Medical Education
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Development of Partially Supervised Kernel-based Proximity Clustering Frameworks and Their Applications
DownloadSpring 2011
The focus of this study is the development and evaluation of a new partially supervised learning framework. This framework belongs to an emerging field in machine learning that augments unsupervised learning processes with some elements of supervision. It is based on proximity fuzzy clustering,...
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Spring 2013
Many machine learning algorithms learn from the data by capturing certain interesting characteristics. Decision trees are used in many classification tasks. In some circumstances, we only want to consider fixed-depth trees. Unfortunately, finding the optimal depth-d decision tree can require time...
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Fall 2015
This dissertation first introduces the concepts of robust active learning (also called optimal experimental design in statistics), and the possible advantages of it over the traditional passive learning method. Then a general regression problem with possibly misspecified models is presented, and...
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The Effectiveness of Team Based Learning on Learning Outcomes in Health Professions Education: A Best Evidence for Medical Education (BEME) Systematic Review
Download2011-10-25
Hartling, Lisa, Oswald, Anna E., Hillier, Tracey, Campbell, Sandy, Fatmi, Mim S.
The aim of this systematic review was to asess the effectiveness of team based learning on improving learning outcomes in health professions education in order to provide curriculum planners with more direction in their decision-making with regard to TBL implementation.