Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
Gunnar Rätsch, Sebastian Mika, Alex Smola
In this paper we consider formulations of multi-class problems based on a generalized notion of a margin and using output coding. This includes, but is not restricted to, standard multi-class SVM formulations. Differ- ently from many previous approaches we learn the code as well as the embedding function. We illustrate how this can lead to a formulation that allows for solving a wider range of problems with for instance many classes or even “missing classes”. To keep our optimization problems tractable we propose an algorithm capable of solving them using two- class classiﬁers, similar in spirit to Boosting.