The differential geometric structure in supervised learning of classifiers
In this thesis, we study the overfitting problem in supervised learning of classifiers from a geometric perspective. As with many inverse problems, learning a classification function from a given set of example-label pairs is an ill-posed problem, i.e., there exist infinitely many classification fun...
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| Language: | en_US |
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2017
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| Online Access: | https://hdl.handle.net/2144/22449 |