Abstract
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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.