Classification using geometric level sets

A variational level set method is developed for the supervised classification problem. Nonlinear classifier decision boundaries are obtained by minimizing an energy functional that is composed of an empirical risk term with a margin-based loss and a geometric regularization term new to machine learn...

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Bibliographic Details
Main Authors: Varshney, Kush R. (Contributor), Willsky, Alan S. (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor)
Format: Article
Language:English
Published: Association for Computing Machinery (ACM), 2012-08-07T13:08:32Z.
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