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|dc.contributor.author||Da Rocha Neto,AR||en|
|dc.description.abstract||In this paper we introduce a new conceptualization for the reduction of the number of support vectors (SVs) for an efficient design of support vector machines. The techniques here presented provide a good balance between SVs reduction and generalization capability. Our proposal explores concepts from classification with reject option. These methods output a third class (the rejected instances) for a binary problem when a prediction cannot be given with sufficient confidence. Rejected instances along with misclassified ones are discarded from the original data to give rise to a classification problem that can be linearly solved. Our experimental study on two benchmark datasets show significant gains in terms of SVs reduction with competitive performances.||en|
|dc.title||Reject option paradigm for the reduction of support vectors||en|
|Appears in Collections:||CTM - Articles in International Conferences|
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