10.5 - Multiclass SVM

10.5 - Multiclass SVM

The SVM as defined so far works for binary classification. What happens if the number of classes is more than two?

One-versus-All: If the number of classes is K > 2 then K different 2-class SVM classifiers are fitted where one class is compared with the rest of the classes combined. A new observation is classified according to where the classifier value is the largest.

One-versus-One: All \(\binom{K}{2}\) pairwise classifiers are fitted and a test observation is classified in the class which wins in the majority of the cases.

The latter method is preferable but if K is too large, the former is to be used.

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