Classification using geometric level sets
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varshney10a.pdf
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Author(s)
Varshney, Kush R.
Willsky, Alan S.
Date Issued
February 2010
Journal
Journal of Machine Learning Research
Publisher
Association for Computing Machinery (ACM)
Citation
Kush R. Varshney and Alan S. Willsky. 2010. Classification Using Geometric Level Sets. J. Mach. Learn. Res. 11 (March 2010), 491-516.
Version
Final published version
Abstract
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 learning: the surface area of the decision boundary. This geometric level set classifier is analyzed in terms of consistency and complexity through the calculation of its ε-entropy. For multicategory classification, an efficient scheme is developed using a logarithmic number of decision functions in the number of classes rather than the typical linear number of decision functions. Geometric level set classification yields performance results on benchmark data sets that are competitive with well-established methods.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://dl.acm.org/citation.cfm?id=1756020
