Learning with the Set Covering Machine
Mario Marchand, John S. Shawe-Taylor · 2001
We generalize the classical algorithms of Valiant and Haussler for learning conjunctions and disjunctions of Boolean attributes to the problem of learning these functions over arbitrary sets of features. The result is a general-purposed learning machine, suitable for practical learning tasks, that we call the Set Covering Machine. We present a version of the Set Covering Machine that uses generalized balls for its set of features and compare its performance to the famous Support Vector Machine. 1 Motivation We may attribute the eectiveness of Support Vector Machines [6] to the fact that they combine two very good ideas. First, they map the space of input vectors onto a very high-dimensional feature space in such a way that nonlinear decision functions on the input space can be constructed by using only hyperplanes on the feature space. Second, they construct the separating hyperplane on the feature space which has the largest possible margin. Vapnik has shown [6] that good ge...