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
SVMs with More than Two Classes
The concept of separating hyperplanes only really lends itself well to the binary classification setting.
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.
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.