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Machine Learning, Evolutionary Algorithms, and the Inference of Mathematical Truths

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dc.contributor.advisor Doboli, Alex en_US
dc.contributor.author Hensley, Asher en_US
dc.contributor.other Department of Electrical Engineering. en_US
dc.date.accessioned 2017-09-20T16:52:45Z
dc.date.available 2017-09-20T16:52:45Z
dc.date.issued 2013-12-01 en_US
dc.identifier.uri http://hdl.handle.net/11401/77467 en_US
dc.description 219 pg. en_US
dc.description.abstract In this thesis we set out to find whether the true data generating formula behind a set of data points can be automatically inferred from the data points alone. We start with the topic of machine learning and quickly realize that black box models can only approximate the real world which creates the motivation to move on to evolutionary algorithms as a vehicle to implement symbolic regression. Through a series of experiments we discover that the mean-squared error cost function is easily fooled by decoy solutions and is unable to make use of all the information presented in the training examples. Based on this result we develop the concept of feature signatures which uniquely define a set of training examples and possess several desirable properties, the most important being invariance to linear transformations. Armed with this concept we conduct several more numerical experiments based on common analytical functions and real world data sets which ultimately lead to the experimental evidence we need to support the thesis. en_US
dc.description.sponsorship This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. en_US
dc.format Monograph en_US
dc.format.medium Electronic Resource en_US
dc.language.iso en_US en_US
dc.publisher The Graduate School, Stony Brook University: Stony Brook, NY. en_US
dc.subject.lcsh Electrical engineering en_US
dc.subject.other symbolic regression en_US
dc.title Machine Learning, Evolutionary Algorithms, and the Inference of Mathematical Truths en_US
dc.type Thesis en_US
dc.mimetype Application/PDF en_US
dc.contributor.committeemember Murray, John. en_US


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