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Skip to Search Results- 3Active learning
- 3Experimental design
- 1Estimability
- 1Fixed-depth decision tree
- 1Fuzzy clustering
- 1Graph clustering
- 1Ben-Zvi, Amos (Chemical and Materials Engineering)
- 1Biao Huang (Chemical and Materials Engineering)
- 1Greiner, Russell (Computing Science)
- 1Pedrycz, Witold (Electrical and Computer Engineering)
- 1Szepesvari, Csaba (Computing Science)
- 1Wiens, Douglas (Department of Mathematical and Statistical Sciences)
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Spring 2011
One of heavy oils upgrading processes is hydroconversion. As it is a complex process involving many chemical reactions, the mathematical model of hydroconversion process often has more kinetic parameters than can be estimated from the data. In this thesis, a model for hydroconversion processing...
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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 2011
Optimal experiment design has been considered as an effective tool to improve model reliability and accuracy in nonlinear system identification in the past few decades. This thesis is concerned with the following challenges which have not been previously addressed: poor initial guess problem of...
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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...