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- 1Experimental design
- 1Fixed-depth decision tree
- 1Fuzzy clustering
- 1Graph clustering
- 1Kernel-based clustering
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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...