AWhat changed
Extended the support-vector approach beyond perfectly separable training data, using linear decision surfaces in transformed feature spaces and evaluating classification on character-recognition tasks.
Scientific scope
Extended the support-vector approach beyond perfectly separable training data, using linear decision surfaces in transformed feature spaces and evaluating classification on character-recognition tasks. Scope limitation: The paper explicitly builds on the earlier separable case. This is a bounded methodological and experimental contribution, not guaranteed superiority over every classifier or a proof that training-set accuracy ensures generalization.
Evidence
Assessment uses the catalogue-linked source evidence for the stated contribution. The paper explicitly builds on the earlier separable case. This is a bounded methodological and experimental contribution, not guaranteed superiority over every classifier or a proof that training-set accuracy ensures generalization.
Contributors
- Corinna Cortes · SCIENCE: Named source contributor; exact labor shares are not measured.
- Vladimir Vapnik · SCIENCE: Named source contributor; exact labor shares are not measured.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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