Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)
Large margin linear classification methods have been successfully ap(cid:173) plied to many applications. For a linearly separable problem, it is known that under appropriate assumptions, the expected misclassification error of the computed "optimal hyperplane" approaches zero at a rate propor(cid:173) tional to the inverse training sample size. This rate is usually charac(cid:173) terized by the margin and the maximum norm of the input data. In this paper, we argue that another quantity, namely the robustness of the in(cid:173) put data distribution, also plays an important role in characterizing the convergence behavior of expected misclassification error. Based on this concept of robustness, we show that for a large margin separable linear classification problem, the expected misclassification error may converge exponentially in the number of training sample size.