When there are K > 2 classes, we are able to use a one-vs-all classification. The process involves comparing individual K classes to the remaining K - 1 classes. It trains one classifier per class in total K classifiers. We fit K SVMs for each class to differentiate that class from all of the other classes. The final step is to assign the observation to the class which resulted in the largest parameters because we are more confident that the test observation belongs to that class compared to all of the other classes.