svm-multiclass-ova-vs-ovo

IN premise — entries/2026/06/21/wiki-Support_vector_machine-chunk-5.md

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

Multiclass SVM uses one-versus-all (OVA: K classifiers, winner-takes-all on calibrated scores) or one-versus-one (OVO: K(K-1)/2 classifiers, max-wins voting).

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.