The Benefits of Crossing Disciplines in Artificial Intelligence

The additional sparse features can be incorporated into the linear model, similar to kernel methods used in machine learning. As mentioned, these solutions have now allowed a non-linear decision boundary in conjunction with a linear classifier. In other words, since a straight line could not accurately represent the distribution of our data, we are now able to accurately represent the distribution of the two classes with a non-linear decision boundary, which make take the form of a curved line or multiple lines. But we're representing that non-linear boundary with a linear classifier so that our results will be interpretable.