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Two criteria for model selection of multiclass support vector machines

Journal Article


Abstract


  • Practical applications call for efficient model selection

    criteria for multiclass support vector machine (SVM)

    classification. To solve this problem, this paper develops two model

    selection criteria by combining or redefining the radius–margin

    bound used in binary SVMs. The combination is justified by

    linking the test error rate of a multiclass SVM with that of a set of

    binary SVMs. The redefinition, which is relatively heuristic, is inspired

    by the conceptual relationship between the radius–margin

    bound and the class separability measure. Hence, the two criteria

    are developed from the perspective of model selection rather than

    a generalization of the radius–margin bound for multiclass SVMs.

    As demonstrated by extensive experimental study, the minimization

    of these two criteria achieves good model selection on most

    data sets. Compared with the k-fold cross validation which is

    often regarded as a benchmark, these two criteria give rise to

    comparable performance with much less computational overhead,

    particularly when a large number of model parameters are to be

    optimized.

Authors


  •   Wang, Lei
  •   Xue, Ping (external author)
  •   Chan, Kap Luk. (external author)

Publication Date


  • 2008

Citation


  • Wang, L., Xue, P. & Chan, K. Luk. (2008). Two criteria for model selection of multiclass support vector machines. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, 38 (6), 1432-1448.

Scopus Eid


  • 2-s2.0-57049126274

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/642

Has Global Citation Frequency


Number Of Pages


  • 16

Start Page


  • 1432

End Page


  • 1448

Volume


  • 38

Issue


  • 6

Place Of Publication


  • New York

Abstract


  • Practical applications call for efficient model selection

    criteria for multiclass support vector machine (SVM)

    classification. To solve this problem, this paper develops two model

    selection criteria by combining or redefining the radius–margin

    bound used in binary SVMs. The combination is justified by

    linking the test error rate of a multiclass SVM with that of a set of

    binary SVMs. The redefinition, which is relatively heuristic, is inspired

    by the conceptual relationship between the radius–margin

    bound and the class separability measure. Hence, the two criteria

    are developed from the perspective of model selection rather than

    a generalization of the radius–margin bound for multiclass SVMs.

    As demonstrated by extensive experimental study, the minimization

    of these two criteria achieves good model selection on most

    data sets. Compared with the k-fold cross validation which is

    often regarded as a benchmark, these two criteria give rise to

    comparable performance with much less computational overhead,

    particularly when a large number of model parameters are to be

    optimized.

Authors


  •   Wang, Lei
  •   Xue, Ping (external author)
  •   Chan, Kap Luk. (external author)

Publication Date


  • 2008

Citation


  • Wang, L., Xue, P. & Chan, K. Luk. (2008). Two criteria for model selection of multiclass support vector machines. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, 38 (6), 1432-1448.

Scopus Eid


  • 2-s2.0-57049126274

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/642

Has Global Citation Frequency


Number Of Pages


  • 16

Start Page


  • 1432

End Page


  • 1448

Volume


  • 38

Issue


  • 6

Place Of Publication


  • New York