Abstract
We study the problem of designing support vector classifiers with respect to a Neyman-Pearson criterion. Specifically, given a user-specified level α ε (0,1), how can we ensure a false alarm rate no greater than α while minimizing the miss rate? We examine two approaches, one based on shifting the offset of a conventionally trained SVM and the other based on the introduction of classspecific weights. Our contributions include a novel heuristic for improved error estimation and a strategy for efficiently searching the parameter space of the second method. We also provide a characterization of the feasible parameter set of the 2ν-SVM on which the second approach is based. The proposed methods are compared on four benchmark datasets.
Original language | English (US) |
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Title of host publication | 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings |
Volume | 5 |
State | Published - Dec 1 2006 |
Event | 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France Duration: May 14 2006 → May 19 2006 |
Other
Other | 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 |
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Country/Territory | France |
City | Toulouse |
Period | 5/14/06 → 5/19/06 |
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering