Kernel methods achieve this by learning from instances. They do not apply some standard computational logic to all the features of each input. Instead they remember each training example, and associate a weight representing its relevance to the achievement of the objective. This could be called instance-based learning. There are several types of Support Vector models including linear, polynomial, RBF, and sigmoid.
