Two criteria for model selection of multiclass support vector machines

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.