Asymptotically Optimal Choice of ε-Loss for Support Vector Machines
Under the assumption of asymptotically unbiased estimators we show that there exists a nontrivial choice of the insensitivity parameter in Vapnik’s e-insensitive loss function which scales linearly with the input noise of the training data. This finding is backed by experimental results.