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Title:Trustworthiness and the importance of graph structure
Author(s):Mayhew, Stephen
Advisor(s):Roth, Dan
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Trustworthiness
data fusion
crowdsourcing
machine learning
natural language processing
Abstract:We begin by giving a comprehensive literature review that ties together many fields which have heretofore remained separate. We comment on the approaches from each field and show which algorithms are similar and which are different. Then, starting from a concrete task, we extend traditional trustworthiness algorithms to deal with the more complex situation of multiclass list-valued trustworthiness. In addition, we introduce a learned predictive method based on standard classification algorithms. In the last section, we explore the theory of trustworthiness and begin to make progress towards charting the space of all trustworthiness graphs. We address the commonly underestimated importance of the structure of a trust- worthiness graph, and define a space in which to work as well as defining the solvability of a trustworthiness graph. Finally, we provide recommendations for future work.
Issue Date:2014-05-30
URI:http://hdl.handle.net/2142/49620
Rights Information:Copyright 2014 Stephen Mayhew
Date Available in IDEALS:2014-05-30
Date Deposited:2014-05


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