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Browsing Electronic Theses and Dissertations by Subject "Machine Learning"

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Browsing Electronic Theses and Dissertations by Subject "Machine Learning"

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  • Wilson, Aaron (Aaron Creighton) (2012-07-28)
    How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. Thi ...
  • Hao, Guohua (2009-07-21)
    Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In t ...
  • Wynkoop, Michael S. (2008-06-09)
    This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the ...
  • Yu, Jun (2013-12-03)
    Citizen Science is a paradigm in which volunteers from the general public participate in scientific studies, often by performing data collection. This paradigm is especially useful if the scope of the study is too broad ...
  • Bjarnason, Ronald V. (2009-12-01)
    This thesis presents a progression of novel planning algorithms that culminates in a new family of diverse Monte-Carlo methods for probabilistic planning domains. We provide a proof for performance guarantees and analyz ...
  • Lam, Michael (Michael Quang Thai) (2014-05-15)
    This thesis addresses a basic problem in computer vision, that of semantic labeling of images. Our work is aimed at object detection in biological images for evolutionary biology research. In particular, our goal is to d ...
  • Parker, Charles (Charles Lincoln) (2007-09-18)
    The goal of many machine learning problems can be formalized as the creation of a function that can properly classify an input vector, given a set of examples of that function. While this formalism has produced a number ...
  • Lin, Junyuan (2013-02-18)
    Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many appli ...

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