What happen when SVM has to predict more than two class?

svm

#1

I am studying about the SVM classifier while studying it I understand that for classification we label classes as +1 and -1.Try to draw the hyperplane which separates them .I want to know this algorithm says we have to classify the classes of dependent variable as +1 and -1 but what will happen when we have more than two classes to predict the dependent variable


#2

Hi,

SVMs with More than Two Classes

The concept of separating hyperplanes only really lends itself well to the binary classification setting.

•one-versus-one

•one-versus-all

One-Vs-One Classification

This approach constructs all pairs of K to compare all the classes in a two-class setting, and build a classifier for each one. For each of these classifiers we classify a test observation and tally the number of times that the test observation is assigned to each of the K classes. The final classification is performed by assigning the test observation to the class which it was most frequently assigned in these pairwise classifications.

One-Vs-All Classification

Here we fit K SMV’s, each time comparing one of the K classes to the remaining K - 1 classes. We then assign the observation to the class for which has the highest amount of confidence that it belongs to the kth class rather than any other classes.

Regards,
Tony