A Review on Consistency and Robustness Properties of Support Vector Machines for Heavy-Tailed Distributions Vrije Universiteit Brussel
Support vector machines (SVMs) belong to the class of modern statistical machine learning techniques and can be described as M-estimatorswith aHilbert norm regularization term for functions. SVMs are consistent and robust for classification and regression purposes if based on a Lipschitz continuous loss and a bounded continuous kernel with a dense reproducing kernel Hilbert space. For regression, one of the conditions used is that the output ...