GAPS IN SUPPORT VECTOR OPTIMIZATION
Conference
·
OSTI ID:985890
- Los Alamos National Laboratory
We show that the stopping criteria used in many support vector machine (SVM) algorithms working on the dual can be interpreted as primal optimality bounds which in turn are known to be important for the statistical analysis of SVMs. To this end we revisit the duality theory underlying the derivation of the dual and show that in many interesting cases primal optimality bounds are the same as known dual optimality bounds.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 985890
- Report Number(s):
- LA-UR-07-0621; TRN: US201017%%68
- Resource Relation:
- Conference: 20TH CONFERENCE ON LEARNING THEORY ; 200706 ; SAN DIEGO
- Country of Publication:
- United States
- Language:
- English
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