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dc.contributor.advisor Baraniuk, Richard G.
dc.creatorDavenport, Mark A.
dc.date.accessioned 2009-06-03T21:12:12Z
dc.date.available 2009-06-03T21:12:12Z
dc.date.issued 2007
dc.identifier.urihttps://hdl.handle.net/1911/20500
dc.description.abstract In binary classification there are two types of errors, and in many applications these may have very different costs. We consider two learning frameworks that address this issue: minimax classification, where we seek to minimize the maximum of the false alarm and miss rates, and Neyman-Pearson (NP) classification, where we seek to minimize the miss rate while ensuring the false alarm rate is less than a specified level a. We show that our approach, based on cost-sensitive support vector machines, significantly outperforms methods typically used in practice. Our results also illustrate the importance of heuristics for improving the accuracy of error rate estimation in this setting. We then reduce anomaly detection to NP classification by considering a second class of points, allowing us to estimate minimum volume sets using algorithms for NP classification. Comparing this approach with traditional one-class methods, we find that our approach has several advantages.
dc.format.extent 80 p.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectElectronics
Electrical engineering
Computer science
dc.title Error control for support vector machines
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Computer Science
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Masters
thesis.degree.name Master of Science
dc.identifier.citation Davenport, Mark A.. "Error control for support vector machines." (2007) Master’s Thesis, Rice University. https://hdl.handle.net/1911/20500.


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