SVMs are inherently two-class classifiers. In particular, the most prevalent method of multi-class classification in practice has been to create |C| one-versus-rest classifiers (commonly referred to as one-versus-all (OVA) classification) where |C| is the number of classes and to choose the class that classifies the test datum with the highest margin. Another approach is to develop a set of one-versus-one classifiers and to select the class that is chosen by the most classifiers. While this involves building |C|(|C| - 1)/2 classifiers, the time for training classifiers may decrease, since the training data set for each classifier is much smaller.
Now let's quickly jump onto how you can apply multi-class classification using SVMs with the help of a real-life dataset.
For the purpose of this section, we will work with the UCI Human Activity Recognition using smartphones dataset, which we are free to use for non-commercial purposes. So make sure not to use this in your groundbreaking autonomous start-up company before obtaining a corresponding software license.
The dataset can be obtained from the Kaggle website, https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones. There you should find a Download button that leads you to a file called https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones/downloads/human-activity-recognition-with-smartphones.zip/1.