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.
|Advisor:||Forbus, Kenneth D.|
|Commitee:||Birnbaum, Lawrence, Downey, Douglas|
|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|
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