TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Luss, Ronny
A1  - d’Aspremont, Alexandre
T1  - Support vector machine classification with indefinite kernels
JF  - Mathematical Programming Computation
N2  - We propose a method for support vector machine classification using indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex loss function, our algorithm simultaneously computes support vectors and a proxy kernel matrix used in forming the loss. This can be interpreted as a penalized kernel learning problem where indefinite kernel matrices are treated as noisy observations of a true Mercer kernel. Our formulation keeps the problem convex and relatively large problems can be solved efficiently using the projected gradient or analytic center cutting plane methods. We compare the performance of our technique with other methods on several standard data sets.
AB  - We propose a method for support vector machine classification using indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex loss function, our algorithm simultaneously computes support vectors and a proxy kernel matrix used in forming the loss. This can be interpreted as a penalized kernel learning problem where indefinite kernel matrices are treated as noisy observations of a true Mercer kernel. Our formulation keeps the problem convex and relatively large problems can be solved efficiently using the projected gradient or analytic center cutting plane methods. We compare the performance of our technique with other methods on several standard data sets.
KW  - Software
KW  - Theoretical Computer Science
Y1  - 2009
SN  - 1867-2949
SS  - 1867-2949
U6  - https://doi.org/10.1007/s12532-009-0005-5
DO  - https://doi.org/10.1007/s12532-009-0005-5
VL  - 1
IS  - 2-3
SP  - 97
EP  - 118
S1  - 22
PB  - Springer Science and Business Media LLC
ER  -