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Electrical Engineering and Computer Science (EECS) Ph.D Dissertation Defense
"Expectation Maximization Algorithm for Optimization of Piecewise-constant Models"
Electrical Engineering and Computer Science
University of California, Merced
AbstractThe Expectation-Maximization (EM) Algorithm is well-known in the literature of machine learning and has been widely used for training of probabilistic and some non-probabilistic models, such as mixture of Gaussians and K-means, respectively. Despite the vast volume of research on application of the EM algorithm for training probabilistic models, there has been little attempt toward usage of the EM algorithm for non-probabilistic models. In this dissertation, various piecewise constant models (such as prototype nearest neighbor models), and their learning procedures in the literature are reviewed. For each model, the EM-based optimization of reviewed model is proposed. The EM algorithms proposed in this dissertation have the same spirit as the original EM algorithm. For each model, the proposed EM algorithm is properly modified to fit the non-probabilistic nature of the model. The EM algorithm was originally designed to fit the modular structure of any intelligent model, such as neural networks or mixture models. In this dissertation, it is shown how with the EM algorithm it is possible to approach a piecewise constant model as a modular structure and optimize the model based on each module of the structure. We specifically applied the proposed optimization algorithm to synthetic reduced nearest neighbor for classification, adversarial label-poisoning, robust synthetic reduced nearest neighbor and synthetic reduced nearest neighbor for regression.
Pooya Tavallali is currently a Ph.D. candidate at the Department of Electrical Engineering and Computer Science, University of California, Merced. He received his B.Sc. and M.Sc. degrees in Electrical Engineering (Communication Systems) from Shiraz University, in 2013 and 2016, respectively. His research interests include machine learning, optimization algorithms, statistical signal and image processing, and statistical pattern recognition.
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