What is recorded here?
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.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
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.
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People and systems
Corinna Cortes; Vladimir Vapnik
Listed source contributors; individual labor shares are not measured
Research locations and affiliations
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Documented countries / regions: United States
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English catalogue narrative
Support-vector networks for nonseparable data
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
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.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Corinna Cortes; Vladimir Vapnik
Listed source contributors; individual labor shares are not measured