svm-multiclass-one-vs-one-outperforms-one-vs-all

IN premise — entries/2026/06/21/wiki-Support_vector_machine-chunk-6.md

Created 2026-06-21T09:55:54+00:00

For multiclass SVM, one-vs-one generally outperforms one-vs-all (Hsu & Lin 2002; Duan & Keerthi 2005).

Dependents

These beliefs depend on this one:

• IN svm-multiclass-requires-architectural-extension — SVMs' binary-native design requires substantial architectural extension for multiclass problems — decomposition into one-vs-all or one-vs-one subproblems, Platt scaling for probability calibration in OVA, or the unified Crammer-Singer formulation — with OVO generally outperforming OVA despite training more classifiers.