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Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification
October 2010 (vol. 32 no. 10)
pp. 1888-1898
ASCII Text
x 
 
Mark A. Davenport, Richard G. Baraniuk, Clayton D. Scott, "Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 10, pp. 1888-1898, October, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2010.29,
author = {Mark A. Davenport and Richard G. Baraniuk and Clayton D. Scott},
title = {Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {32},
number = {10},
issn = {0162-8828},
year = {2010},
pages = {1888-1898},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.29},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification
IS - 10
SN - 0162-8828
SP1888
EP1898
EPD - 1888-1898
A1 - Mark A. Davenport,
A1 - Richard G. Baraniuk,
A1 - Clayton D. Scott,
PY - 2010
KW - Minimax classification
KW - Neyman-Pearson classification
KW - support vector machine
KW - error estimation
KW - parameter selection.
VL - 32
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Mark A. Davenport, Stanford University, Stanford
Richard G. Baraniuk, Rice University, Houston
Clayton D. Scott, University of Michigan, Ann Arbor
This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and Neyman-Pearson criteria. In principle, these criteria can be optimized in a straightforward way using a cost-sensitive SVM. In practice, however, because these criteria require especially accurate error estimation, standard techniques for tuning SVM parameters, such as cross-validation, can lead to poor classifier performance. To address this issue, we first prove that the usual cost-sensitive SVM, here called the 2C-SVM, is equivalent to another formulation called the 2\nu-SVM. We then exploit a characterization of the 2\nu-SVM parameter space to develop a simple yet powerful approach to error estimation based on smoothing. In an extensive experimental study, we demonstrate that smoothing significantly improves the accuracy of cross-validation error estimates, leading to dramatic performance gains. Furthermore, we propose coordinate descent strategies that offer significant gains in computational efficiency, with little to no loss in performance.

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Index Terms:
Minimax classification, Neyman-Pearson classification, support vector machine, error estimation, parameter selection.
Citation:
Mark A. Davenport, Richard G. Baraniuk, Clayton D. Scott, "Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 10, pp. 1888-1898, Oct. 2010, doi:10.1109/TPAMI.2010.29
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