every row of is a classifier for one of the classes
As we saw above, every row of xi=0 would always give score of zero regardless of the weights, so all lines would be forced to cross the origin.
Interpretation of linear classifiers as template matching. Another interpretation for the weights corresponds to a template (or sometimes also called a prototype) for one of the classes. The score of each class for an image is then obtained by comparing each template with the image using an inner product (or dot product) one by one to find the one that “fits” best. With this terminology, the linear classifier is doing template matching, where the templates are learned. Another way to think of it is that we are still effectively doing Nearest Neighbor, but instead of having thousands of training images we are only using a single image per class (although we will learn it, and it does not necessarily have to be one of the images in the training set), and we use the (negative) inner product as the distance instead of the L1 or L2 distance.
http://cs231n.github.io/linear-classify/