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Browsing Electronic Theses and Dissertations by Subject "Machine Learning"

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Bayesian methods for knowledge transfer and policy search in reinforcement learning

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 ...
Efficient training and feature induction in sequential supervised learning

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 ...
Learning MDP action models via discrete mixture trees

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 ...
Monte-Carlo planning for probabilistic domains

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 ...
Structured gradient boosting

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 ...
A study of methods for fine-grained object classification of arthropod specimens

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 ...