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Dissertation/Thesis Abstract

Statistical Relational Learning through Structural Analogy and Probabilistic Generalization
by Halstead, Daniel T., Ph.D., Northwestern University, 2011, 159; 3488474
Abstract (Summary)

My primary research motivation is the development of a truly generic Machine Learning engine. Towards this, I am exploring the interplay between feature-based representations of data, for which there are powerful statistical machine learning algorithms, and structured representations, which are useful for reasoning and are capable of representing a broader spectrum of information. This places my work in the emergent field of Statistical Relational Learning. I combine the two approaches to representation by using analogy to translate back and forth from a relational space to a reduced feature space. Analogy allows us to narrow the search space by singling out structural likenesses in the data (which become the features) rather than relations, and also gives us a similarity metric for doing unsupervised learning. In the process, we gain several insights about the nature of analogy, and the relationship between similarity and probability.

Indexing (document details)
Advisor: Forbus, Kenneth D.
Commitee: Birnbaum, Lawrence, Downey, Douglas
School: Northwestern University
Department: Electrical and Computer Engineering
School Location: United States -- Illinois
Source: DAI-B 73/04, Dissertation Abstracts International
Subjects: Computer Engineering, Artificial intelligence
Keywords: Machine learning, Probabilistic generalization, Statistical relational learning, Structural analogy
Publication Number: 3488474
ISBN: 978-1-267-08479-8
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